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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-12-02
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">152</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">13</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-line-chart"></i> 平均评分:</span>
                    <span id="avg-score" class="font-semibold text-secondary">1.9</span>
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                <span id="display-count" class="font-medium">显示 152 篇论文 (共 152 篇)</span>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01372v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>面向频率自适应多模态推荐的结构化谱推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wei Yang, Rui Zhong, Yiqun Chen, Chi Lu, Peng Jiang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究多模态推荐中因模态噪声、语义不一致和图传播不稳定导致的性能下降问题。其核心思想是提出结构化频谱推理框架，通过图引导变换将多模态信号分解为频谱带，利用带掩码和低秩跨带交互进行可靠性调制与融合，以频域视角实现自适应、鲁棒的多模态特征建模。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对多模态推荐的核心挑战，提出结构化频谱推理框架，通过频域分析处理异构数据噪声与不一致性，与关注异构数据统一建模的VLM类比高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 07:39:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01372v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01372v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information, but remains challenged by modality-specific noise, semantic inconsistency, and unstable propagation over user-item graphs. These issues are often exacerbated by naive fusion or shallow modeling strategies, leading to degraded generalization and poor robustness. While recent work has explored the frequency domain as a lens to separate stable from noisy signals, most methods rely on static filtering or reweighting, lacking the ability to reason over spectral structure or adapt to modality-specific reliability. To address these challenges, we propose a Structured Spectral Reasoning (SSR) framework for frequency-aware multimodal recommendation. Our method follows a four-stage pipeline: (i) Decompose graph-based multimodal signals into spectral bands via graph-guided transformations to isolate semantic granularity; (ii) Modulate band-level reliability with spectral band masking, a training-time masking with a prediction-consistency objective that suppresses brittle frequency components; (iii) Fuse complementary frequency cues using hyperspectral reasoning with low-rank cross-band interaction; and (iv) Align modality-specific spectral features via contrastive regularization to promote semantic and structural consistency. Experiments on three real-world benchmarks show consistent gains over strong baselines, particularly under sparse and cold-start settings. Additional analyses indicate that structured spectral modeling improves robustness and provides clearer diagnostics of how different bands contribute to performance.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01925v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>从优化视角修正大语言模型的思维过程
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Rectifying LLM Thought from Lens of Optimization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junnan Liu, Hongwei Liu, Songyang Zhang, Kai Chen
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何解决大语言模型在长链思维推理中出现的过度思考、推理链过长等次优行为。其核心思想是将链式思维过程视为梯度下降优化过程，并提出了RePro方法，通过定义评估推理优化过程的代理目标函数（使用强度和稳定性双评分机制），生成过程级奖励来指导模型后训练优化。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文从优化视角分析LLM推理过程，提出过程级奖励方法RePro，直接针对LLM在推理任务中的核心应用与性能优化，与关注领域高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:41:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01925v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01925v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advancements in large language models (LLMs) have been driven by their emergent reasoning capabilities, particularly through long chain-of-thought (CoT) prompting, which enables thorough exploration and deliberation. Despite these advances, long-CoT LLMs often exhibit suboptimal reasoning behaviors, such as overthinking and excessively protracted reasoning chains, which can impair performance. In this paper, we analyze reasoning processes through an optimization lens, framing CoT as a gradient descent procedure where each reasoning step constitutes an update toward problem resolution. Building on this perspective, we introduce RePro (Rectifying Process-level Reward), a novel approach to refine LLM reasoning during post-training. RePro defines a surrogate objective function to assess the optimization process underlying CoT, utilizing a dual scoring mechanism to quantify its intensity and stability. These scores are aggregated into a composite process-level reward, seamlessly integrated into reinforcement learning with verifiable rewards (RLVR) pipelines to optimize LLMs. Extensive experiments across multiple reinforcement learning algorithms and diverse LLMs, evaluated on benchmarks spanning mathematics, science, and coding, demonstrate that RePro consistently enhances reasoning performance and mitigates suboptimal reasoning behaviors.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01710v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>MMAG：面向大语言模型应用的混合记忆增强生成
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            MMAG: Mixed Memory-Augmented Generation for Large Language Models Applications
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Stefano Zeppieri
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM在持续交互中难以维持相关性、个性化和连续性的问题，其核心思想是借鉴认知心理学，构建包含对话、长期用户、事件关联、情境感知和工作记忆五个交互层的混合记忆增强生成框架，通过分层记忆组织与协调机制来提升语言代理的连贯性和主动性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的混合记忆增强生成框架直接针对LLM在推荐/搜索/广告中持续交互、个性化、上下文一致性的核心痛点，其多层级记忆架构与异构数据处理理念高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 14:16:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01710v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01710v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) excel at generating coherent text within a single prompt but fall short in sustaining relevance, personalization, and continuity across extended interactions. Human communication, however, relies on multiple forms of memory, from recalling past conversations to adapting to personal traits and situational context. This paper introduces the Mixed Memory-Augmented Generation (MMAG) pattern, a framework that organizes memory for LLM-based agents into five interacting layers: conversational, long-term user, episodic and event-linked, sensory and context-aware, and short-term working memory. Drawing inspiration from cognitive psychology, we map these layers to technical components and outline strategies for coordination, prioritization, and conflict resolution. We demonstrate the approach through its implementation in the Heero conversational agent, where encrypted long-term bios and conversational history already improve engagement and retention. We further discuss implementation concerns around storage, retrieval, privacy, and latency, and highlight open challenges. MMAG provides a foundation for building memory-rich language agents that are more coherent, proactive, and aligned with human needs.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.02008v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>大型语言模型测试时计算扩展的艺术
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            The Art of Scaling Test-Time Compute for Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aradhye Agarwal, Ayan Sengupta, Tanmoy Chakraborty
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">主题：研究大型语言模型在推理阶段如何动态分配计算资源以提升推理能力。核心思想：通过大规模实验发现，最优计算分配策略取决于模型类型、问题难度和计算预算，并据此提出策略选择框架。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文系统研究推理时计算分配策略，直接对应LLM技术核心的效率优化，对推荐/搜索系统的实时推理成本控制有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:59:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02008v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02008v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Test-time scaling (TTS) -- the dynamic allocation of compute during inference -- is a promising direction for improving reasoning in large language models (LLMs). However, a systematic comparison of well-known TTS strategies under identical conditions is missing, and the influence of model type and problem difficulty on performance remains unclear. To address these gaps, we conduct the first large-scale study of TTS, spanning over thirty billion tokens generated using eight open-source LLMs (7B to 235B parameters), across four reasoning datasets. We observe three consistent trends: (1) no single TTS strategy universally dominates; (2) reasoning models exhibit distinct trace-quality patterns across problem difficulty and trace length, forming short-horizon and long-horizon categories; and (3) for a given model type, the optimal TTS performance scales monotonically with compute budget. Based on these insights, we provide a practical recipe for selecting the best TTS strategy, considering problem difficulty, model type, and compute budget, providing a practical guide to effective inference-time scaling.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01420v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>PromptBridge：面向大语言模型的跨模型提示迁移方法
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            PromptBridge: Cross-Model Prompt Transfer for Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yaxuan Wang, Quan Liu, Zhenting Wang, Zichao Li, Wei Wei, Yang Liu, Yujia Bao
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM应用中因模型切换导致的提示性能下降问题（模型漂移）。其核心方法是提出PromptBridge框架，通过少量对齐任务校准，利用模型自适应反射提示演化获取最优提示对，并学习跨模型提示映射，实现无需重新优化的提示迁移。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接解决LLM应用中的核心工程挑战——模型漂移问题，其提出的跨模型提示迁移框架对搜索、推荐和广告系统中多模型部署与切换具有重要实用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 08:55:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01420v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01420v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) underpin applications in code generation, mathematical reasoning, and agent-based workflows. In practice, systems access LLMs via commercial APIs or open-source deployments, and the model landscape (e.g., GPT, Claude, Llama) evolves rapidly. This rapid evolution forces frequent model switches driven by capability, cost, deployment constraints, and privacy. Yet prompts are highly model-sensitive: reusing a prompt engineered for one model on another often yields substantially worse performance than a prompt optimized for the target model. We term this phenomenon Model Drifting. Through extensive empirical analysis across diverse LLM configurations, we show that model drifting is both common and severe. To address this challenge, we introduce PromptBridge, a training-free framework that preserves prompt effectiveness under model switches, enabling cross-model prompt transfer without costly per-task or per-model re-optimization. PromptBridge requires only a small set of alignment tasks for calibration. It first applies Model-Adaptive Reflective Prompt Evolution (MAP-RPE) to obtain task- and model-specific optimal prompts via iterative reflective refinement and quantitative evaluation. Using the resulting calibrated prompt pairs for the source and target models, PromptBridge learns a cross-model prompt mapping. At test time, i.e., for an unseen task, given a source-model prompt, this mapping directly produces an optimized prompt for the target model. Experiments in single-agent and multi-agent settings show that PromptBridge consistently improves downstream accuracy while reducing migration effort. The code will be available soon.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01675v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>GRASP：基于样本划分的引导残差适配器
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            GRASP: Guided Residual Adapters with Sample-wise Partitioning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Felix Nützel, Mischa Dombrowski, Bernhard Kainz
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究长尾分布下文本到图像扩散模型的模式崩溃问题，其核心方法是利用外部先验对样本进行静态分区以最小化组内梯度冲突，并通过向Transformer前馈层注入特定集群的残差适配器来微调预训练模型，从而提升尾部类别的生成质量和多样性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的GRASP方法通过静态样本分区和残差适配器微调来解决长尾分布下的梯度冲突问题，其核心思想（静态分区、残差适配器注入）与Transformer架构效率、异构数据统一建模（将不同类别视为不同模态）以及直接LLM应用中的适配器技术高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 13:43:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01675v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01675v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in text-to-image diffusion models enable high-fidelity generation across diverse prompts. However, these models falter in long-tail settings, such as medical imaging, where rare pathologies comprise a small fraction of data. This results in mode collapse: tail-class outputs lack quality and diversity, undermining the goal of synthetic data augmentation for underrepresented conditions. We pinpoint gradient conflicts between frequent head and rare tail classes as the primary culprit, a factor unaddressed by existing sampling or conditioning methods that mainly steer inference without altering the learned distribution. To resolve this, we propose GRASP: Guided Residual Adapters with Sample-wise Partitioning. GRASP uses external priors to statically partition samples into clusters that minimize intra-group gradient clashes. It then fine-tunes pre-trained models by injecting cluster-specific residual adapters into transformer feedforward layers, bypassing learned gating for stability and efficiency. On the long-tail MIMIC-CXR-LT dataset, GRASP yields superior FID and diversity metrics, especially for rare classes, outperforming baselines like vanilla fine-tuning and Mixture of Experts variants. Downstream classification on NIH-CXR-LT improves considerably for tail labels. Generalization to ImageNet-LT confirms broad applicability. Our method is lightweight, scalable, and readily integrates with diffusion pipelines.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01179v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>迈向在线广告点击率预测基准：数据集、评估协议与展望
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Toward a benchmark for CTR prediction in online advertising: datasets, evaluation protocols and perspectives
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shan Gao, Yanwu Yang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究在线广告中点击率预测模型的标准化评估问题，核心思想是设计一个统一的基准平台，提供灵活的数据集接口、模型组件和全面的评估协议体系，以促进模型比较和理解。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文构建了CTR预测的基准平台和评估体系，直接服务于广告领域的模型开发与评估，属于核心领域进展，但未涉及LLM技术本身或创新架构。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 01:36:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01179v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01179v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This research designs a unified architecture of CTR prediction benchmark (Bench-CTR) platform that offers flexible interfaces with datasets and components of a wide range of CTR prediction models. Moreover, we construct a comprehensive system of evaluation protocols encompassing real-world and synthetic datasets, a taxonomy of metrics, standardized procedures and experimental guidelines for calibrating the performance of CTR prediction models. Furthermore, we implement the proposed benchmark platform and conduct a comparative study to evaluate a wide range of state-of-the-art models from traditional multivariate statistical to modern large language model (LLM)-based approaches on three public datasets and two synthetic datasets. Experimental results reveal that, (1) high-order models largely outperform low-order models, though such advantage varies in terms of metrics and on different datasets; (2) LLM-based models demonstrate a remarkable data efficiency, i.e., achieving the comparable performance to other models while using only 2% of the training data; (3) the performance of CTR prediction models has achieved significant improvements from 2015 to 2016, then reached a stage with slow progress, which is consistent across various datasets. This benchmark is expected to facilitate model development and evaluation and enhance practitioners' understanding of the underlying mechanisms of models in the area of CTR prediction. Code is available at https://github.com/NuriaNinja/Bench-CTR.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01171v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>在线广告转化率预测：建模技术、性能评估与未来方向
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tao Xue, Yanwu Yang, Panyu Zhai
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文系统综述了在线广告转化率预测的建模技术，核心贡献在于将现有模型按底层技术分为六类，并阐述了各类技术间的关联与演进关系，为领域提供了清晰的技术图谱。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文是对在线广告转化率预测的全面文献综述，系统分类了现有技术并讨论了未来方向，直接关联广告领域的核心建模问题，但未聚焦于LLM或Transformer架构等前沿技术。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 01:02:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01171v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01171v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Conversion and conversion rate (CVR) prediction play a critical role in efficient advertising decision-making. In past decades, although researchers have developed plenty of models for CVR prediction, the methodological evolution and relationships between different techniques have been precluded. In this paper, we conduct a comprehensive literature review on CVR prediction in online advertising, and classify state-of-the-art CVR prediction models into six categories with respect to the underlying techniques and elaborate on connections between these techniques. For each category of models, we present the framework of underlying techniques, their advantages and disadvantages, and discuss how they are utilized for CVR prediction. Moreover, we summarize the performance of various CVR prediction models on public and proprietary datasets. Finally, we identify research trends, major challenges, and promising future directions. We observe that results of performance evaluation reported in prior studies are not unanimous; semantics-enriched, attribution-enhanced, debiased CVR prediction and jointly modeling CTR and CVR prediction would be promising directions to explore in the future. This review is expected to provide valuable references and insights for future researchers and practitioners in this area.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01546v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>LPCD：从分层到子模块量化的统一框架
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            LPCD: Unified Framework from Layer-Wise to Submodule Quantization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuma Ichikawa, Yudai Fujimoto, Akira Sakai
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何超越传统逐层量化方法，更准确地量化大型语言模型中的复杂子模块。其核心思想是提出LPCD统一框架，通过优化任意子模块的松弛目标并结合逐层量化器投影，实现对复杂子模块的量化同时保持逐层量化的效率优势。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出统一量化框架LPCD，通过子模块优化提升LLM量化效果，直接属于使能Transformer技术中的效率优化方向，对大规模推荐系统的模型部署有实际应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 11:21:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01546v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01546v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">stat.ML</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Post-training quantization (PTQ) aims to preserve model-level behavior; however, most methods focus on individual linear layers. Even recent extensions, such as QEP and LoaQ, which mitigate error propagation or target specific submodules, still rely on layer-wise formulations and fail to capture the behavior of larger submodules. We introduce Layer-Projected Coordinate Descent (LPCD), a unified framework that extends PTQ beyond layers by optimizing relaxed objectives across arbitrary submodules and projecting the solutions with layer-wise quantizers. LPCD generalizes existing methods and provides a principled approach to quantizing complex submodules while maintaining the efficiency and compatibility of layer-wise PTQ pipelines. Across diverse LLM architectures and bit-widths, LPCD-based submodule quantization consistently enhances both layer-wise PTQ methods and existing submodule approaches.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.02014v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>TUNA：驯服统一视觉表示以实现原生统一多模态模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            TUNA: Taming Unified Visual Representations for Native Unified Multimodal Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiheng Liu, Weiming Ren, Haozhe Liu, Zijian Zhou, Shoufa Chen, Haonan Qiu, Xiao...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何为统一多模态模型构建统一的视觉表示空间，核心思想是通过级联VAE编码器和表示编码器创建连续的统一视觉表示，使图像和视频能在同一空间中进行理解和生成任务。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出统一视觉表示空间的核心思想，与VLM处理异构数据的类比高度相关，但其应用场景聚焦视觉模态而非推荐/搜索的典型异构数据。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:59:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02014v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02014v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Unified multimodal models (UMMs) aim to jointly perform multimodal understanding and generation within a single framework. We present TUNA, a native UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows end-to-end processing of images and videos for both understanding and generation tasks. Compared to prior UMMs with decoupled representations, TUNA's unified visual space avoids representation format mismatches introduced by separate encoders, outperforming decoupled alternatives in both understanding and generation. Moreover, we observe that stronger pretrained representation encoders consistently yield better performance across all multimodal tasks, highlighting the importance of the representation encoder. Finally, in this unified setting, jointly training on both understanding and generation data allows the two tasks to benefit from each other rather than interfere. Our extensive experiments on multimodal understanding and generation benchmarks show that TUNA achieves state-of-the-art results in image and video understanding, image and video generation, and image editing, demonstrating the effectiveness and scalability of its unified representation design.
                </div>
            </details>
    </div>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01461v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>保持独特，保持高效：在多任务模型融合中保留模型个性
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kuangpu Guo, Yuhe Ding, Jian Liang, Zilei Wang, Ran He
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究多任务模型合并中任务特异性信息丢失导致性能下降的问题，核心方法是提出DTS框架，通过奇异值分解保留关键任务特征，并引入分组阈值缩放策略来高效融合多任务知识。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的多任务模型合并框架DTS，通过分解、阈值和缩放技术保留任务特异性信息，直接关联推荐/搜索系统中的多任务学习与模型效率问题，属于核心领域进展。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 09:47:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01461v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01461v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Model merging has emerged as a promising paradigm for enabling multi-task capabilities without additional training. However, existing methods often experience substantial performance degradation compared with individually fine-tuned models, even on similar tasks, underscoring the need to preserve task-specific information. This paper proposes Decomposition, Thresholding, and Scaling (DTS), an approximation-based personalized merging framework that preserves task-specific information with minimal storage overhead. DTS first applies singular value decomposition to the task-specific information and retains only a small subset of singular values and vectors. It then introduces a novel thresholding strategy that partitions singular vector elements into groups and assigns a scaling factor to each group. To enable generalization to unseen tasks, we further extend DTS with a variant that fuses task-specific information in a data-free manner based on the semantic similarity of task characteristics. Extensive experiments demonstrate that DTS consistently outperforms state-of-the-art baselines while requiring only 1\% additional storage per task. Furthermore, experiments on unseen tasks show that the DTS variant achieves significantly better generalization performance. Our code is available at https://github.com/krumpguo/DTS.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.02004v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>AlignSAE：概念对齐的稀疏自编码器
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            AlignSAE: Concept-Aligned Sparse Autoencoders
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Minglai Yang, Xinyu Guo, Mihai Surdeanu, Liangming Pan
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM隐藏激活中事实知识的可解释性表示问题。其核心方法是提出AlignSAE，通过“预训练后微调”的课程学习，将稀疏自编码器特征与预定义本体对齐，创建可分离、可干预的概念化特征接口。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种改进稀疏自编码器特征与人类概念对齐的方法，属于LLM可解释性技术，对理解LLM内部表示有潜在应用价值，但未直接涉及推荐、搜索或广告系统。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:58:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02004v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02004v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) encode factual knowledge within hidden parametric spaces that are difficult to inspect or control. While Sparse Autoencoders (SAEs) can decompose hidden activations into more fine-grained, interpretable features, they often struggle to reliably align these features with human-defined concepts, resulting in entangled and distributed feature representations. To address this, we introduce AlignSAE, a method that aligns SAE features with a defined ontology through a "pre-train, then post-train" curriculum. After an initial unsupervised training phase, we apply supervised post-training to bind specific concepts to dedicated latent slots while preserving the remaining capacity for general reconstruction. This separation creates an interpretable interface where specific relations can be inspected and controlled without interference from unrelated features. Empirical results demonstrate that AlignSAE enables precise causal interventions, such as reliable "concept swaps", by targeting single, semantically aligned slots.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01949v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Script：面向多模态大语言模型的图结构及查询条件语义令牌剪枝
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhongyu Yang, Dannong Xu, Wei Pang, Yingfang Yuan
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究多模态大语言模型中视觉令牌过多导致的计算效率问题，核心思想是设计一个无需重新训练的即插即用剪枝框架，通过图结构模块去除视觉冗余令牌，并结合查询条件语义模块保留与用户查询相关的视觉信息。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种针对多模态大语言模型的图结构查询条件令牌剪枝方法，虽然核心是视觉令牌优化而非直接应用于推荐/搜索/广告，但其图结构建模和查询条件保留机制与推荐系统中的用户-项目交互图建模和查询/上下文感知有潜在类比价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:59:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01949v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01949v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The rapid growth of visual tokens in multimodal large language models (MLLMs) leads to excessive memory consumption and inference latency, especially when handling high-resolution images and videos. Token pruning is a technique used to mitigate this issue by removing redundancy, but existing methods often ignore relevance to the user query or suffer from the limitations of attention mechanisms, reducing their adaptability and effectiveness. To address these challenges, we propose Script, a plug-and-play pruning method that requires no retraining and generalizes across diverse MLLMs. Script comprises two modules: a graph-structured pruning module that removes visually redundant tokens, and a query-conditioned semantic pruning module that preserves query-relevant visual information. Together, they enhance performance on multimodal tasks. Experiments on fourteen benchmarks across image and video understanding tasks show that Script consistently achieves higher model efficiency and predictive accuracy compared to existing pruning methods. On LLaVA-NeXT-7B, it achieves up to 6.8x prefill speedup and 10x FLOP reduction, while retaining 96.88% of the original performance.
                </div>
            </details>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.02010v1" target="_blank" rel="noopener noreferrer">
                四比六：采用自适应块缩放的更精确NVFP4量化
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jack Cook, Junxian Guo, Guangxuan Xiao, Yujun Lin, Song Han
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及模型量化技术，属于LLM效率优化的范畴，可能通过减少内存占用和加速推理来间接支持推荐/搜索系统中的大规模模型部署。然而，标题未明确说明其与推荐/搜索/广告系统的直接关联，且量化技术属于通用优化方法，而非专门针对这些领域的核心进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:59:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02010v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02010v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As large language models have grown larger, low-precision numerical formats such as NVFP4 have become increasingly popular due to the speed and memory benefits they provide. However, to accelerate computation with NVFP4, all matrix multiplication operands--weights and activations in the forward pass, and weights, activations, and gradients in the backward pass--must be quantized to NVFP4, often leading to divergence during training and performance degradation during inference. NVFP4 by evaluating multiple potential scale factors for each block of values. To address this issue, in this work we introduce Four Over Six (4/6), a modification to the NVFP4 quantization algorithm that evaluates two potential scale factors for each block of values. Unlike integer formats, floating-point formats such as FP4 have the most quantization error on near-maximal values in each block, which we find to be primarily responsible for downstream performance degradation. We find that for some blocks, scaling to smaller FP4 values makes the distribution of representable values more uniform, improving representation of near-maximal values. Importantly, 4/6 can be implemented efficiently on NVIDIA Blackwell GPUs, making it viable to use while training LLMs with NVFP4. In pre-training experiments with transformer and hybrid model architectures, we find that 4/6 prevents divergence in several cases, bringing training loss significantly closer to BF16 compared to models trained with current state-of-the-art NVFP4 training recipes. We also find that 4/6 can be easily incorporated into many different post-training quantization methods and generally improves downstream accuracy. We hope this inspires future work in training and deploying models with NVFP4.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01979v1" target="_blank" rel="noopener noreferrer">
                链式基础：通过迭代推理与参考反馈改进图形用户界面基础
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Chain-of-Ground: Improving GUI Grounding via Iterative Reasoning and Reference Feedback
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aiden Yiliu Li, Bizhi Yu, Daoan Lei, Tianhe Ren, Shilong Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注图形用户界面（GUI）的基础技术，属于人机交互和计算机视觉的交叉领域。虽然涉及迭代推理技术，但其核心应用场景（GUI理解与交互）与推荐系统、搜索或广告的排名和建模任务关联较弱，不属于当前关注的直接应用或使能技术范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:37:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01979v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01979v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
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                    GUI grounding aims to align natural language instructions with precise regions in complex user interfaces. Advanced multimodal large language models show strong ability in visual GUI grounding but still struggle with small or visually similar targets and ambiguity in real world layouts. These limitations arise from limited grounding capacity and from underuse of existing reasoning potential. We present Chain of Ground CoG a training free multi step grounding framework that uses multimodal large language models for iterative visual reasoning and refinement. Instead of direct prediction the model progressively reflects and adjusts its hypotheses leading to more accurate and interpretable localization. Our approach achieves 68.4 accuracy on the ScreenSpot Pro benchmark an improvement of 4.8 points. To measure real world generalization we introduce TPanel UI a dataset of 420 labeled industrial control panels with visual distortions such as blur and masking. On TPanel UI Chain of Ground improves over the strong baseline Qwen3 VL 235B by 6.9 points showing the effectiveness of multi step training free grounding across real world and digital interfaces. These results highlight a direction for unlocking grounding potential through structured iterative refinement instead of additional training.
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            <a href="https://www.alphaxiv.org/abs/2512.01909v1" target="_blank" rel="noopener noreferrer">
                潜在辩论：一种用于解释大语言模型思维的代理框架
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Latent Debate: A Surrogate Framework for Interpreting LLM Thinking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lihu Chen, Xiang Yin, Francesca Toni
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其核心关注点是大语言模型（LLM）的可解释性，这属于LLM评估和基准测试范畴，而非直接应用于推荐系统、搜索或广告。虽然可解释性在工业应用中很重要，但该标题未暗示任何具体的推荐/搜索/广告应用场景，也未涉及Transformer架构改进或异构数据处理等当前关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:27:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01909v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01909v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Understanding the internal thinking process of Large Language Models (LLMs) and the cause of hallucinations remains a key challenge. To this end, we introduce latent debate, a novel framework for interpreting model predictions through the lens of implicit internal arguments. Unlike the current work of self-consistency and multi-agent debate, which relies on explicit debates among multiple answers or multiple models, latent debate captures the hidden supporting and attacking signals that arise within a single model during a single inference. We first present a model- and task-agnostic conceptual framework, and then instantiate it symbolically to approximate the thinking process of LLMs on True/False prediction tasks. Empirical studies demonstrate that latent debate is a faithful structured surrogate model that has highly consistent predictions with the original LLM. Beyond interpretability, we demonstrate that latent debate provides a strong baseline for hallucination detection. Further analysis reveals strong correlations between hallucinations and debate patterns, such as a high degree of latent debates in the middle layers is linked to a higher risk of hallucinations. These findings position latent debate as a potential framework for understanding internal mechanisms of LLMs, especially for scenarios where internal (dis)agreements appear during the inference steps.
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            <a href="https://www.alphaxiv.org/abs/2512.01728v1" target="_blank" rel="noopener noreferrer">
                推理未言之事：基于遗漏感知图推理的虚假信息检测
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhengjia Wang, Danding Wang, Qiang Sheng, Jiaying Wu, Juan Cao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及虚假信息检测，属于内容安全领域，与推荐系统、搜索或广告的核心技术（如排序、召回、用户建模）关联较弱。虽然图推理技术可能间接应用于用户-物品交互建模，但论文焦点是虚假信息而非推荐/搜索性能优化，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 14:37:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01728v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01728v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    This paper investigates the detection of misinformation, which deceives readers by explicitly fabricating misleading content or implicitly omitting important information necessary for informed judgment. While the former has been extensively studied, omission-based deception remains largely overlooked, even though it can subtly guide readers toward false conclusions under the illusion of completeness. To pioneer in this direction, this paper presents OmiGraph, the first omission-aware framework for misinformation detection. Specifically, OmiGraph constructs an omission-aware graph for the target news by utilizing a contextual environment that captures complementary perspectives of the same event, thereby surfacing potentially omitted contents. Based on this graph, omission-oriented relation modeling is then proposed to identify the internal contextual dependencies, as well as the dynamic omission intents, formulating a comprehensive omission relation representation. Finally, to extract omission patterns for detection, OmiGraph introduces omission-aware message-passing and aggregation that establishes holistic deception perception by integrating the omission contents and relations. Experiments show that, by considering the omission perspective, our approach attains remarkable performance, achieving average improvements of +5.4% F1 and +5.3% ACC on two large-scale benchmarks.
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            <a href="https://www.alphaxiv.org/abs/2512.01374v1" target="_blank" rel="noopener noreferrer">
                利用大型语言模型稳定强化学习：公式与实践
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Stabilizing Reinforcement Learning with LLMs: Formulation and Practices
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chujie Zheng, Kai Dang, Bowen Yu, Mingze Li, Huiqiang Jiang, Junrong Lin, Yuqion...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及强化学习（RL）与LLMs的结合，属于RL论文范畴。根据您的关注点，RL论文若无明确与推荐系统/搜索/广告的相关性，则被视为不相关。标题未明确说明其在RecSys/Search/Ads中的潜在应用，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 07:45:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01374v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01374v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper proposes a novel formulation for reinforcement learning (RL) with large language models, explaining why and under what conditions the true sequence-level reward can be optimized via a surrogate token-level objective in policy gradient methods such as REINFORCE. Specifically, through a first-order approximation, we show that this surrogate becomes increasingly valid only when both the training-inference discrepancy and policy staleness are minimized. This insight provides a principled explanation for the crucial role of several widely adopted techniques in stabilizing RL training, including importance sampling correction, clipping, and particularly Routing Replay for Mixture-of-Experts (MoE) models. Through extensive experiments with a 30B MoE model totaling hundreds of thousands of GPU hours, we show that for on-policy training, the basic policy gradient algorithm with importance sampling correction achieves the highest training stability. When off-policy updates are introduced to accelerate convergence, combining clipping and Routing Replay becomes essential to mitigate the instability caused by policy staleness. Notably, once training is stabilized, prolonged optimization consistently yields comparable final performance regardless of cold-start initialization. We hope that the shared insights and the developed recipes for stable RL training will facilitate future research.
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            <a href="https://www.alphaxiv.org/abs/2512.01816v1" target="_blank" rel="noopener noreferrer">
                Envision：面向因果世界过程洞察的统一理解与生成基准测试
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            <i class="fa fa-star mr-1"></i>3/10
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            Envision: Benchmarking Unified Understanding & Generation for Causal World Process Insights
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Juanxi Tian, Siyuan Li, Conghui He, Lijun Wu, Cheng Tan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及统一理解和生成基准测试，可能属于评估基准范畴，这与您排除的“Hallucination, Evaluation benchmarks, or other purely NLP-centric topics”相关。虽然提及“因果世界过程洞察”可能暗示多模态或序列建模，但缺乏明确指向推荐系统、搜索或广告的具体应用场景，且标题未表明涉及Transformer架构改进或LLM技术在这些领域的直接应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 15:52:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01816v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01816v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Current multimodal models aim to transcend the limitations of single-modality representations by unifying understanding and generation, often using text-to-image (T2I) tasks to calibrate semantic consistency. However, their reliance on static, single-image generation in training and evaluation leads to overfitting to static pattern matching and semantic fusion, while fundamentally hindering their ability to model dynamic processes that unfold over time. To address these constraints, we propose Envision-a causal event progression benchmark for chained text-to-multi-image generation. Grounded in world knowledge and structured by spatiotemporal causality, it reorganizes existing evaluation dimensions and includes 1,000 four-stage prompts spanning six scientific and humanities domains. To transition evaluation from single images to sequential frames and assess whether models truly internalize world knowledge while adhering to causal-temporal constraints, we introduce Envision-Score, a holistic metric integrating multi-dimensional consistency, physicality, and aesthetics. Comprehensive evaluation of 15 models (10 specialized T2I models, 5 unified models) uncovers: specialized T2I models demonstrate proficiency in aesthetic rendering yet lack intrinsic world knowledge. Unified multimodal models bridge this gap, consistently outperforming specialized counterparts in causal narrative coherence. However, even these unified architectures remain subordinate to closed-source models and struggle to overcome the core challenge of spatiotemporal consistency. This demonstrates that a focus on causally-isolated single images impedes multi-frame reasoning and generation, promoting static pattern matching over dynamic world modeling-ultimately limiting world knowledge internalization, generation.
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            <a href="https://www.alphaxiv.org/abs/2512.01636v1" target="_blank" rel="noopener noreferrer">
                联合视觉-语言空间中的生成式编辑用于零样本组合图像检索
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Generative Editing in the Joint Vision-Language Space for Zero-Shot Composed Image Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xin Wang, Haipeng Zhang, Mang Li, Zhaohui Xia, Yueguo Chen, Yu Zhang, Chunyu Wei
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及视觉-语言模型（VLM）技术，但其核心应用是组合图像检索，这属于纯粹的视觉检索任务，与推荐系统、搜索或广告中的文本/序列数据建模没有直接关联。虽然VLM技术可能启发异构数据处理，但论文标题明确指向图像检索这一特定应用领域，而非通用的异构数据建模方法。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 13:04:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01636v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01636v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Composed Image Retrieval (CIR) enables fine-grained visual search by combining a reference image with a textual modification. While supervised CIR methods achieve high accuracy, their reliance on costly triplet annotations motivates zero-shot solutions. The core challenge in zero-shot CIR (ZS-CIR) stems from a fundamental dilemma: existing text-centric or diffusion-based approaches struggle to effectively bridge the vision-language modality gap. To address this, we propose Fusion-Diff, a novel generative editing framework with high effectiveness and data efficiency designed for multimodal alignment. First, it introduces a multimodal fusion feature editing strategy within a joint vision-language (VL) space, substantially narrowing the modality gap. Second, to maximize data efficiency, the framework incorporates a lightweight Control-Adapter, enabling state-of-the-art performance through fine-tuning on only a limited-scale synthetic dataset of 200K samples. Extensive experiments on standard CIR benchmarks (CIRR, FashionIQ, and CIRCO) demonstrate that Fusion-Diff significantly outperforms prior zero-shot approaches. We further enhance the interpretability of our model by visualizing the fused multimodal representations.
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            <a href="https://www.alphaxiv.org/abs/2512.01550v1" target="_blank" rel="noopener noreferrer">
                NavForesee：一种用于分层规划和双视野导航预测的统一视觉-语言世界模型
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            <i class="fa fa-star mr-1"></i>3/10
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            NavForesee: A Unified Vision-Language World Model for Hierarchical Planning and Dual-Horizon Navigation Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fei Liu, Shichao Xie, Minghua Luo, Zedong Chu, Junjun Hu, Xiaolong Wu, Mu Xu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然该论文涉及视觉-语言模型（VLM）的统一建模，这在一定程度上与“VLM类比用于异构数据”的焦点相关，但其核心应用领域是机器人导航和规划，而非推荐系统、搜索或广告。标题中的“分层规划”和“导航预测”表明其专注于空间任务，缺乏明确的RecSys/Search/Ads应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 11:24:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01550v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01550v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                    Embodied navigation for long-horizon tasks, guided by complex natural language instructions, remains a formidable challenge in artificial intelligence. Existing agents often struggle with robust long-term planning about unseen environments, leading to high failure rates. To address these limitations, we introduce NavForesee, a novel Vision-Language Model (VLM) that unifies high-level language planning and predictive world model imagination within a single, unified framework. Our approach empowers a single VLM to concurrently perform planning and predictive foresight. Conditioned on the full instruction and historical observations, the model is trained to understand the navigation instructions by decomposing the task, tracking its progress, and formulating the subsequent sub-goal. Simultaneously, it functions as a generative world model, providing crucial foresight by predicting short-term environmental dynamics and long-term navigation milestones. The VLM's structured plan guides its targeted prediction, while the imagined future provides rich context to inform the navigation actions, creating a powerful internal feedback loop of perception-planning/prediction-action. We demonstrate through extensive experiments on the R2R-CE and RxR-CE benchmark that NavForesee achieves highly competitive performance in complex scenarios. Our work highlights the immense potential of fusing explicit language planning with implicit spatiotemporal prediction, paving the way for more intelligent and capable embodied agents.
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            <a href="https://www.alphaxiv.org/abs/2512.01540v1" target="_blank" rel="noopener noreferrer">
                FlashVGGT：基于压缩描述符注意力机制的高效可扩展视觉几何变换器
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            FlashVGGT: Efficient and Scalable Visual Geometry Transformers with Compressed Descriptor Attention
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zipeng Wang, Dan Xu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉几何变换器的效率优化，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然提到了注意力机制的改进，但这是针对视觉几何任务的特定优化，没有展示在推荐/搜索/广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 11:12:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01540v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01540v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    3D reconstruction from multi-view images is a core challenge in computer vision. Recently, feed-forward methods have emerged as efficient and robust alternatives to traditional per-scene optimization techniques. Among them, state-of-the-art models like the Visual Geometry Grounding Transformer (VGGT) leverage full self-attention over all image tokens to capture global relationships. However, this approach suffers from poor scalability due to the quadratic complexity of self-attention and the large number of tokens generated in long image sequences. In this work, we introduce FlashVGGT, an efficient alternative that addresses this bottleneck through a descriptor-based attention mechanism. Instead of applying dense global attention across all tokens, FlashVGGT compresses spatial information from each frame into a compact set of descriptor tokens. Global attention is then computed as cross-attention between the full set of image tokens and this smaller descriptor set, significantly reducing computational overhead. Moreover, the compactness of the descriptors enables online inference over long sequences via a chunk-recursive mechanism that reuses cached descriptors from previous chunks. Experimental results show that FlashVGGT achieves reconstruction accuracy competitive with VGGT while reducing inference time to just 9.3% of VGGT for 1,000 images, and scaling efficiently to sequences exceeding 3,000 images. Our project page is available at https://wzpscott.github.io/flashvggt_page/.
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            <a href="https://www.alphaxiv.org/abs/2512.01312v1" target="_blank" rel="noopener noreferrer">
                IVCR-200K：面向交互式视频语料库检索的大规模多轮对话基准
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            IVCR-200K: A Large-Scale Multi-turn Dialogue Benchmark for Interactive Video Corpus Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ning Han, Yawen Zeng, Shaohua Long, Chengqing Li, Sijie Yang, Dun Tan, Jianfeng ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频检索的对话基准构建，属于特定领域的数据集创建工作。虽然涉及检索和对话，但未明确涉及推荐系统、搜索或广告的核心进展，也未展示LLM或Transformer架构的创新。其潜在应用可能局限于视频检索领域，与当前关注的RecSys/Search/Ads核心进展或使能技术关联较弱。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:12:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01312v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01312v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In recent years, significant developments have been made in both video retrieval and video moment retrieval tasks, which respectively retrieve complete videos or moments for a given text query. These advancements have greatly improved user satisfaction during the search process. However, previous work has failed to establish meaningful "interaction" between the retrieval system and the user, and its one-way retrieval paradigm can no longer fully meet the personalization and dynamic needs of at least 80.8\% of users. In this paper, we introduce the Interactive Video Corpus Retrieval (IVCR) task, a more realistic setting that enables multi-turn, conversational, and realistic interactions between the user and the retrieval system. To facilitate research on this challenging task, we introduce IVCR-200K, a high-quality, bilingual, multi-turn, conversational, and abstract semantic dataset that supports video retrieval and even moment retrieval. Furthermore, we propose a comprehensive framework based on multi-modal large language models (MLLMs) to help users interact in several modes with more explainable solutions. The extensive experiments demonstrate the effectiveness of our dataset and framework.
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            <a href="https://www.alphaxiv.org/abs/2512.01992v1" target="_blank" rel="noopener noreferrer">
                LLM CHESS：通过国际象棋对大型语言模型的推理与指令遵循能力进行基准测试
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            LLM CHESS: Benchmarking Reasoning and Instruction-Following in LLMs through Chess
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sai Kolasani, Maxim Saplin, Nicholas Crispino, Kyle Montgomery, Jared Quincy Dav...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的推理能力和指令遵循的基准测试，属于纯粹的LLM评估范畴，与您的关注点（推荐系统、搜索、广告中的核心进展或应用）无直接关联。虽然国际象棋作为测试平台可能涉及推理，但论文未表明这些技术如何应用于您的目标领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:51:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01992v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01992v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    We introduce LLM CHESS, an evaluation framework designed to probe the generalization of reasoning and instruction-following abilities in large language models (LLMs) through extended agentic interaction in the domain of chess. We rank over 50 open and closed source models by playing against a random opponent using a range of behavioral metrics, including win and loss rates, move quality, move legality, hallucinated actions, and game duration. For a subset of top reasoning models, we derive an Elo estimate by playing against a chess engine with variably configured skill, which allows for comparisons between models in an easily understandable way. Despite the simplicity of the instruction-following task and the weakness of the opponent, many state-of-the-art models struggle to complete games or achieve consistent wins. Similar to other benchmarks on complex reasoning tasks, our experiments reveal a clear separation between reasoning and non-reasoning models. However, unlike existing static benchmarks, the stochastic and dynamic nature of LLM CHESS uniquely reduces overfitting and memorization while preventing benchmark saturation, proving difficult even for top reasoning models. To support future work on evaluating reasoning and instruction-following in LLMs, we release our experimental framework, a public leaderboard, and a dataset of associated games.
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            <a href="https://www.alphaxiv.org/abs/2512.01970v1" target="_blank" rel="noopener noreferrer">
                从原子到复合：强化学习实现互补推理中的泛化能力
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            From Atomic to Composite: Reinforcement Learning Enables Generalization in Complementary Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sitao Cheng, Xunjian Yin, Ruiwen Zhou, Yuxuan Li, Xinyi Wang, Liangming Pan, Wil...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及强化学习与推理泛化，但未明确提及推荐系统、搜索或广告领域的应用。虽然强化学习在某些推荐场景中有应用，但标题中的'互补推理'和'泛化'过于通用，缺乏与当前关注领域的直接联系。没有证据表明该研究针对RecSys/Search/Ads的具体问题或具有明确的跨模态建模潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:27:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01970v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01970v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The mechanism by which RL contributes to reasoning capabilities-whether it incentivizes the synthesis of new skills or merely amplifies existing behaviors-remains a subject of intense debate. In this work, we investigate this question through the lens of Complementary Reasoning, a complex task that requires integrating internal parametric knowledge with external contextual information. Using a controlled synthetic dataset of human biographies, we strictly decouple this ability into two atomic skills: Parametric Reasoning (relying on internal knowledge) and Contextual Reasoning (depending on external information). To rigorously assess capability boundaries, we evaluate generalization across three distinct levels of difficulty: I.I.D., Composition, and Zero-shot settings. We find that while SFT is sufficient for in-distribution performance, it struggles with O.O.D. generalization, particularly in Zero-shot settings where relational combinations are novel. Crucially, we identify the SFT Generalization Paradox: Models supervised solely on the composite task achieve near-perfect in-distribution accuracy but collapse on out-of-distribution generalization, indicating their reliance on rote memorization of path shortcuts. In contrast, we find that RL acts as a reasoning synthesizer rather than a probability amplifier. However, we uncover a strict atomic prerequisite: RL can only synthesize these complex strategies if the base model has first mastered the independent atomic skills (Parametric and Contextual) via SFT. These findings challenge the view of RL as a mere amplifier, suggesting that given sufficient atomic foundations, RL can actively synthesize complex reasoning strategies from learned primitives without explicit supervision on such complex strategies. This indicates that decoupled atomic training followed by RL offers a scalable path to generalization for complex reasoning tasks.
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            <a href="https://www.alphaxiv.org/abs/2512.01945v1" target="_blank" rel="noopener noreferrer">
                基于指令-策略协同进化的智能体策略优化
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Agentic Policy Optimization via Instruction-Policy Co-Evolution
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Han Zhou, Xingchen Wan, Ivan Vulić, Anna Korhonen
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及智能体策略优化和指令协同进化，这属于强化学习（RL）领域。虽然RL在推荐系统、搜索或广告中有潜在应用，但标题本身没有明确表明与这些领域的直接相关性，也没有提到任何具体的应用场景或与Transformer/LLM技术的结合。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:56:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01945v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01945v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capability of large language models (LLMs), enabling autonomous agents that can conduct effective multi-turn and tool-integrated reasoning. While instructions serve as the primary protocol for defining agents, RLVR typically relies on static and manually designed instructions. However, those instructions may be suboptimal for the base model, and the optimal instruction may change as the agent's policy improves and explores the interaction with the environment. To bridge the gap, we introduce INSPO, a novel Instruction-Policy co-evolution framework that integrates instruction optimization as a dynamic component of the reinforcement learning (RL) loop. INSPO maintains a dynamic population of instruction candidates that are sampled with questions, where reward signals in RL loops are automatically attributed to each instruction, and low performers are periodically pruned. New instructions are generated and verified through an on-policy reflection mechanism, where an LLM-based optimizer analyzes past experience from a replay buffer and evolves more effective strategies given the current policy. We conduct extensive experiments on multi-turn retrieval and reasoning tasks, demonstrating that INSPO substantially outperforms strong baselines relying on static instructions. INSPO discovers innovative instructions that guide the agent toward more strategic reasoning paths, achieving substantial performance gains with only a marginal increase in computational overhead.
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            <a href="https://www.alphaxiv.org/abs/2512.01896v1" target="_blank" rel="noopener noreferrer">
                OPOR-Bench：评估大型语言模型在在线舆情报告生成任务上的表现
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            OPOR-Bench: Evaluating Large Language Models on Online Public Opinion Report Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinzheng Yu, Yang Xu, Haozhen Li, Junqi Li, Yifan Feng, Ligu Zhu, Hao Shen, Lei ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于LLM在舆情报告生成这一特定内容生成任务上的评估，属于纯粹的LLM应用评估范畴，与您关注的推荐系统、搜索、广告等核心领域以及相关的使能技术无直接关联。虽然涉及LLM，但其应用场景（舆情报告生成）与您排除的“AIGC、内容生成、摘要等纯粹LLM中心化主题”高度重合，且未表明与RecSys/Search/Ads有潜在应用联系。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:18:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01896v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01896v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Online Public Opinion Reports consolidate news and social media for timely crisis management by governments and enterprises. While large language models have made automated report generation technically feasible, systematic research in this specific area remains notably absent, particularly lacking formal task definitions and corresponding benchmarks. To bridge this gap, we define the Automated Online Public Opinion Report Generation (OPOR-GEN) task and construct OPOR-BENCH, an event-centric dataset covering 463 crisis events with their corresponding news articles, social media posts, and a reference summary. To evaluate report quality, we propose OPOR-EVAL, a novel agent-based framework that simulates human expert evaluation by analyzing generated reports in context. Experiments with frontier models demonstrate that our framework achieves high correlation with human judgments. Our comprehensive task definition, benchmark dataset, and evaluation framework provide a solid foundation for future research in this critical domain.
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            <a href="https://www.alphaxiv.org/abs/2512.01865v1" target="_blank" rel="noopener noreferrer">
                用于语音语言模型的跨语言交错技术
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Cross-Lingual Interleaving for Speech Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Adel Moumen, Guangzhi Sun, Philip C. Woodland
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及语音语言模型，属于语音处理领域，与您关注的搜索、推荐、广告等文本/序列建模核心领域相关性较弱。虽然跨语言技术可能对多语言搜索有一定启发，但论文明确聚焦语音而非文本模态，且未提及在推荐/广告系统中的潜在应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 16:48:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01865v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01865v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Spoken Language Models (SLMs) aim to learn linguistic competence directly from speech using discrete units, widening access to Natural Language Processing (NLP) technologies for languages with limited written resources. However, progress has been largely English-centric due to scarce spoken evaluation benchmarks and training data, making cross-lingual learning difficult. We present a cross-lingual interleaving method that mixes speech tokens across languages without textual supervision. We also release an EN-FR training dataset, TinyStories (~42k hours), together with EN-FR spoken StoryCloze and TopicCloze benchmarks for cross-lingual semantic evaluation, both synthetically generated using GPT-4. On 360M and 1B SLMs under matched training-token budgets, interleaving improves monolingual semantic accuracy, enables robust cross-lingual continuation, and strengthens cross-lingual hidden-state alignment. Taken together, these results indicate that cross-lingual interleaving is a simple, scalable route to building multilingual SLMs that understand and converse across languages. All resources will be made open-source to support reproducibility.
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            <a href="https://www.alphaxiv.org/abs/2512.01848v1" target="_blank" rel="noopener noreferrer">
                超越监督微调：基于强化学习提升大型推理模型的安全性与推理能力
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Beyond SFT: Reinforcement Learning for Safer Large Reasoning Models with Better Reasoning Ability
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinghan Jia, Nathalie Baracaldo, Sijia Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注通过强化学习提升LLM的安全性和推理能力，属于LLM核心技术的改进。虽然强化学习本身可能间接应用于推荐系统或搜索的优化，但论文标题明确聚焦于“大型推理模型”和“安全性”，这与您关注的直接应用（如排序、召回）或异构数据建模等具体方向关联较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 16:35:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01848v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01848v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large reasoning models (LRMs) extend large language models by generating explicit chain-of-thought (CoT) reasoning, significantly improving mathematical and logical problem solving. However, this explicit reasoning process also introduces new safety risks, as unsafe behaviors often emerge within intermediate reasoning trajectories, even when final answers appear harmless. Existing safety alignment approaches primarily rely on supervised fine-tuning (SFT) over safety-oriented long CoT datasets. While intuitive, we find that SFT produces inconsistent safety improvements, degrades reasoning ability, and generalizes poorly across model families. These limitations suggest that purely supervised approaches are insufficient for robust safety alignment in LRMs. To address this, we investigate reinforcement learning (RL) as a complementary optimization framework for LRM safety training. Unlike SFT, RL directly optimizes model policies with reward feedback, enabling more adaptive and stable alignment. Extensive experiments across multiple model families and benchmarks show that RL achieves stronger and more consistent safety gains while maintaining reasoning competence. Further analysis of reflection dynamics and token-level entropy reveals that RL suppresses unsafe exploratory reasoning while preserving reflective depth, leading to safer and more reliable reasoning processes.
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            <a href="https://www.alphaxiv.org/abs/2512.01725v1" target="_blank" rel="noopener noreferrer">
                警惕推理过度自信：多解任务推理过程中的陷阱
            </a>
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            Beware of Reasoning Overconfidence: Pitfalls in the Reasoning Process for Multi-solution Tasks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiannan Guan, Qiguang Chen, Libo Qin, Dengyun Peng, Jinhao Liu, Liangyu Huo, Jia...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及推理过程中的过度自信问题，属于推理可靠性和评估范畴。虽然推理是LLM的核心能力，但该标题聚焦于多解任务中的推理陷阱，属于纯粹的NLP评估问题，与推荐系统、搜索或广告中的实际应用缺乏直接关联。没有明确说明这些推理问题如何应用于推荐、搜索或广告领域的具体技术改进。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 14:35:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01725v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01725v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) excel in reasoning tasks requiring a single correct answer, but they perform poorly in multi-solution tasks that require generating comprehensive and diverse answers. We attribute this limitation to \textbf{reasoning overconfidence}: a tendency to express undue certainty in an incomplete solution set. To examine the effect, we introduce \textit{MuSoBench}, a benchmark of multi-solution problems. Experiments show that the conventional short chain-of-thought (Short-CoT) prompting paradigm exhibits pronounced overconfidence, whereas the emerging long chain-of-thought (Long-CoT) approach mitigates it through iterative exploration and self-reflection. We further characterise observable behaviours and influential factors. To probe the underlying cause, we propose the \textbf{cognitive-rigidity hypothesis}, which posits that overconfidence arises when the reasoning process prematurely converges on a narrow set of thought paths. An attention-entropy analysis offers preliminary support for this view. These findings provide tools for assessing the completeness of LLM reasoning and highlight the need to move evaluation beyond single-answer accuracy toward comprehensive exploration.
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            <a href="https://www.alphaxiv.org/abs/2512.01707v1" target="_blank" rel="noopener noreferrer">
                StreamGaze：流媒体视频中基于视线引导的时序推理与主动理解
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            StreamGaze: Gaze-Guided Temporal Reasoning and Proactive Understanding in Streaming Videos
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daeun Lee, Subhojyoti Mukherjee, Branislav Kveton, Ryan A. Rossi, Viet Dac Lai, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频流中的视线引导理解和时序推理，属于计算机视觉与视频理解领域。虽然涉及时序建模，但缺乏与推荐系统、搜索或广告领域的明确关联，也未涉及Transformer架构、LLM技术或异构数据统一建模等当前关注的核心方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 14:15:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01707v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01707v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Streaming video understanding requires models not only to process temporally incoming frames, but also to anticipate user intention for realistic applications like AR glasses. While prior streaming benchmarks evaluate temporal reasoning, none measure whether MLLMs can interpret or leverage human gaze signals within a streaming setting. To fill this gap, we introduce StreamGaze, the first benchmark designed to evaluate how effectively MLLMs use gaze for temporal and proactive reasoning in streaming videos. StreamGaze introduces gaze-guided past, present, and proactive tasks that comprehensively evaluate streaming video understanding. These tasks assess whether models can use real-time gaze to follow shifting attention and infer user intentions from only past and currently observed frames. To build StreamGaze, we develop a gaze-video QA generation pipeline that aligns egocentric videos with raw gaze trajectories via fixation extraction, region-specific visual prompting, and scanpath construction. This pipeline produces spatio-temporally grounded QA pairs that closely reflect human perceptual dynamics. Across all StreamGaze tasks, we observe substantial performance gaps between state-of-the-art MLLMs and human performance, revealing fundamental limitations in gaze-based temporal reasoning, intention modeling, and proactive prediction. We further provide detailed analyses of gaze-prompting strategies, reasoning behaviors, and task-specific failure modes, offering deeper insight into why current MLLMs struggle and what capabilities future models must develop. All data and code will be publicly released to support continued research in gaze-guided streaming video understanding.
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            <a href="https://www.alphaxiv.org/abs/2512.01661v1" target="_blank" rel="noopener noreferrer">
                学习可解性边界：对齐大语言模型以检测不可解问题
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            Learning the Boundary of Solvability: Aligning LLMs to Detect Unsolvable Problems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dengyun Peng, Qiguang Chen, Bofei Liu, Jiannan Guan, Libo Qin, Zheng Yan, Jinhao...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注大语言模型对齐与问题可解性检测，属于纯粹的LLM能力评估范畴。虽然检测不可解问题可能在搜索系统中用于过滤无效查询，但这种应用过于间接且非核心，与当前关注的推荐系统、搜索排序、广告技术等直接应用或使能技术关系微弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 13:32:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01661v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01661v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Ensuring LLM reliability requires not only solving complex problems but also recognizing when a problem is unsolvable. Current models often struggle to distinguish objective unsolvability (inherent contradictions in the problem) from subjective capability limitations (problems beyond the model's competence), which leads to hallucinations and overconfidence. To address this, we propose UnsolvableQA and UnsolvableRL to solve feasible problems, detect inherent contradictions, and prudently refuse tasks beyond capability. Specifically, we construct UnsolvableQA, a dataset of paired solvable and unsolvable instances derived via a dual-track methodology: programmatic generation for logic puzzles and a novel "Reverse Construction" method that injects contradictions into valid reasoning chains for mathematics. Building on this dataset, we introduce UnsolvableRL, a reinforcement learning framework with three reward components jointly accounting for accuracy, unsolvability, and difficulty. Empirical results show that our approach achieves near-perfect unsolvability detection while also improving accuracy on solvable tasks. Crucially, we identify Capability Collapse, demonstrating that explicit exposure to unsolvable data is indispensable for preventing models from becoming systematically overconfident. Our code and data are available at https://github.com/sfasfaffa/unsolvableQA.
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            <a href="https://www.alphaxiv.org/abs/2512.01556v1" target="_blank" rel="noopener noreferrer">
                LEC：线性期望约束在选择性预测与路由系统中的错误发现率控制
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            LEC: Linear Expectation Constraints for False-Discovery Control in Selective Prediction and Routing Systems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiyuan Wang, Aniri, Tianlong Chen, Yue Zhang, Heng Tao Shen, Xiaoshuang Shi, Ka...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及选择性预测和错误发现率控制，这些是机器学习中的通用技术，但未明确针对推荐系统、搜索或广告领域。虽然选择性预测可能间接应用于这些领域（例如路由低置信度查询），但标题未提及任何具体应用或与LLM/Transformer技术的直接联系，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 11:27:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01556v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01556v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) often generate unreliable answers, while heuristic uncertainty methods fail to fully distinguish correct from incorrect predictions, causing users to accept erroneous answers without statistical guarantees. We address this issue through the lens of false discovery rate (FDR) control, ensuring that among all accepted predictions, the proportion of errors does not exceed a target risk level. To achieve this in a principled way, we propose LEC, which reinterprets selective prediction as a constrained decision problem by enforcing a Linear Expectation Constraint over selection and error indicators. Then, we establish a finite-sample sufficient condition, which relies only on a held-out set of exchangeable calibration samples, to compute an FDR-constrained, coverage-maximizing threshold. Furthermore, we extend LEC to a two-model routing mechanism: given a prompt, if the current model's uncertainty exceeds its calibrated threshold, we delegate it to a stronger model, while maintaining a unified FDR guarantee. Evaluations on closed-ended and open-ended question-answering (QA) datasets show that LEC achieves tighter FDR control and substantially improves sample retention over prior methods. Moreover, the two-model routing mechanism achieves lower risk levels while accepting more correct samples than each individual model.
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            <a href="https://www.alphaxiv.org/abs/2512.01512v1" target="_blank" rel="noopener noreferrer">
                MCAT：利用多模态大语言模型将多对多语音到文本翻译扩展至70种语言
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            MCAT: Scaling Many-to-Many Speech-to-Text Translation with MLLMs to 70 Languages
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yexing Du, Kaiyuan Liu, Youcheng Pan, Bo Yang, Keqi Deng, Xie Chen, Yang Xiang, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音到文本翻译的多语言扩展，属于语音处理领域，与推荐系统、搜索或广告的核心技术无直接关联。虽然涉及多模态大语言模型（MLLMs），但其应用场景（语音翻译）与异构数据建模（如上下文特征和用户序列）的视觉-语言模型类比缺乏明确联系，且未提及在推荐/搜索/广告中的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 10:39:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01512v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01512v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal Large Language Models (MLLMs) have achieved great success in Speech-to-Text Translation (S2TT) tasks. However, current research is constrained by two key challenges: language coverage and efficiency. Most of the popular S2TT datasets are substantially English-centric, which restricts the scaling-up of MLLMs' many-to-many translation capabilities. Moreover, the inference speed of MLLMs degrades dramatically when the speech is converted into long sequences (e.g., 750 tokens). To address these limitations, we propose a Multilingual Cost-effective Accelerated Speech-to-Text Translator (MCAT) framework, which includes two innovations. First, a language scaling method that leverages curriculum learning and a data balancing strategy is introduced to extend the language coverage supported by MLLMs to 70 languages and achieve mutual translation among these languages. Second, an optimized speech adapter module is designed to reduce the length of the speech sequence to only 30 tokens. Extensive experiments were conducted on MLLMs of different scales (9B and 27B). The experimental results demonstrate that MCAT not only surpasses state-of-the-art end-to-end models on the FLEURS dataset across 70x69 directions but also enhances batch inference efficiency. This is achieved with only ~100M trainable parameters and by using only 10 hours of S2TT data per language. Furthermore, we have released MCAT as open-source to promote the development of MLLMs for robust S2TT capabilities. The code and models are released at https://github.com/yxduir/m2m-70.
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            <a href="https://www.alphaxiv.org/abs/2512.01460v1" target="_blank" rel="noopener noreferrer">
                提升BERT微调在低资源语言情感分析中的性能
            </a>
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            Enhancing BERT Fine-Tuning for Sentiment Analysis in Lower-Resourced Languages
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jozef Kubík, Marek Šuppa, Martin Takáč
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于BERT在低资源语言情感分析中的微调技术，属于NLP特定任务优化，与推荐系统、搜索或广告的核心进展无直接关联。虽然BERT是Transformer架构，但论文的应用场景（情感分析）和领域（低资源语言）均未体现对RecSys/Search/Ads的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 09:45:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01460v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01460v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Limited data for low-resource languages typically yield weaker language models (LMs). Since pre-training is compute-intensive, it is more pragmatic to target improvements during fine-tuning. In this work, we examine the use of Active Learning (AL) methods augmented by structured data selection strategies which we term 'Active Learning schedulers', to boost the fine-tuning process with a limited amount of training data. We connect the AL to data clustering and propose an integrated fine-tuning pipeline that systematically combines AL, clustering, and dynamic data selection schedulers to enhance model's performance. Experiments in the Slovak, Maltese, Icelandic and Turkish languages show that the use of clustering during the fine-tuning phase together with AL scheduling can simultaneously produce annotation savings up to 30% and performance improvements up to four F1 score points, while also providing better fine-tuning stability.
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            <a href="https://www.alphaxiv.org/abs/2512.01457v1" target="_blank" rel="noopener noreferrer">
                ZIP-RC：零开销推理时奖励与成本预测，用于自适应且可解释的生成
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        <div class="mb-2 text-base text-gray-700">
            ZIP-RC: Zero-overhead Inference-time Prediction of Reward and Cost for Adaptive and Interpretable Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rohin Manvi, Joey Hong, Tim Seyde, Maxime Labonne, Mathias Lechner, Sergey Levin...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题关注推理时的奖励与成本预测，主要与自适应生成相关，这属于纯粹的LLM生成优化范畴。虽然提到了“自适应”，但没有明确指向推荐、搜索或广告领域的特定应用场景，也没有涉及Transformer架构改进或多模态建模等使能技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 09:44:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01457v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01457v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Large language models excel at reasoning but lack key aspects of introspection, including anticipating their own success and the computation required to achieve it. Humans use real-time introspection to decide how much effort to invest, when to make multiple attempts, when to stop, and when to signal success or failure. Without this, LLMs struggle to make intelligent meta-cognition decisions. Test-time scaling methods like Best-of-N drive up cost and latency by using a fixed budget of samples regardless of the marginal benefit of each one at any point in generation, and the absence of confidence signals can mislead people, prevent appropriate escalation to better tools, and undermine trustworthiness. Learned verifiers or reward models can provide confidence estimates, but do not enable adaptive inference and add substantial cost by requiring extra models or forward passes. We present ZIP-RC, an adaptive inference method that equips models with zero-overhead inference-time predictions of reward and cost. At every token, ZIP-RC reuses reserved or unused logits in the same forward pass as next-token prediction to output a joint distribution over final reward and remaining length -- no extra models, architecture change, or inference overhead. This full joint distribution is used to compute a sampling utility which is the linear combination of the expected maximum reward, total compute, and latency of set of samples if generated to completion. During inference, we maximize this utility with meta-actions that determine which prefix of tokens to continue or initiate sampling from. On mixed-difficulty mathematical benchmarks, ZIP-RC improves accuracy by up to 12% over majority voting at equal or lower average cost, and traces smooth Pareto frontiers between quality, compute, and latency. By providing real-time reward-cost introspection, ZIP-RC enables adaptive, efficient reasoning.
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            <a href="https://www.alphaxiv.org/abs/2512.01410v1" target="_blank" rel="noopener noreferrer">
                DyFuLM：一种用于情感分析的高级多模态框架
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        <div class="mb-2 text-base text-gray-700">
            DyFuLM: An Advanced Multimodal Framework for Sentiment Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruohan Zhou, Jiachen Yuan, Churui Yang, Wenzheng Huang, Guoyan Zhang, Shiyao Wei...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及多模态框架和情感分析，可能属于NLP应用领域，与推荐系统、搜索或广告的核心技术关联较弱。虽然多模态处理可能启发异构数据建模，但情感分析本身属于NLP任务，未明确指向RecSys/Search/Ads的具体应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 08:30:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01410v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01410v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Understanding sentiment in complex textual expressions remains a fundamental challenge in affective computing. To address this, we propose a Dynamic Fusion Learning Model (DyFuLM), a multimodal framework designed to capture both hierarchical semantic representations and fine-grained emotional nuances. DyFuLM introduces two key moodules: a Hierarchical Dynamic Fusion module that adaptively integrates multi-level features, and a Gated Feature Aggregation module that regulates cross-layer information ffow to achieve balanced representation learning. Comprehensive experiments on multi-task sentiment datasets demonstrate that DyFuLM achieves 82.64% coarse-grained and 68.48% fine-grained accuracy, yielding the lowest regression errors (MAE = 0.0674, MSE = 0.0082) and the highest R^2 coefficient of determination (R^2= 0.6903). Furthermore, the ablation study validates the effectiveness of each module in DyFuLM. When all modules are removed, the accuracy drops by 0.91% for coarse-grained and 0.68% for fine-grained tasks. Keeping only the gated fusion module causes decreases of 0.75% and 0.55%, while removing the dynamic loss mechanism results in drops of 0.78% and 0.26% for coarse-grained and fine-grained sentiment classification, respectively. These results demonstrate that each module contributes significantly to feature interaction and task balance. Overall, the experimental findings further validate that DyFuLM enhances sentiment representation and overall performance through effective hierarchical feature fusion.
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            <a href="https://www.alphaxiv.org/abs/2512.01369v1" target="_blank" rel="noopener noreferrer">
                MARSAD：一种用于实时社交媒体分析的多功能工具
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            MARSAD: A Multi-Functional Tool for Real-Time Social Media Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Md. Rafiul Biswas, Firoj Alam, Wajdi Zaghouani
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题描述的是社交媒体分析工具，属于应用工具而非核心算法或架构创新。虽然社交媒体数据可能用于推荐系统，但该工具本身并未涉及推荐/搜索/广告领域的核心算法进展、LLM技术、Transformer架构改进或VLM类比建模等当前关注点。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 07:31:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01369v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01369v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    MARSAD is a multifunctional natural language processing (NLP) platform designed for real-time social media monitoring and analysis, with a particular focus on the Arabic-speaking world. It enables researchers and non-technical users alike to examine both live and archived social media content, producing detailed visualizations and reports across various dimensions, including sentiment analysis, emotion analysis, propaganda detection, fact-checking, and hate speech detection. The platform also provides secure data-scraping capabilities through API keys for accessing public social media data. MARSAD's backend architecture integrates flexible document storage with structured data management, ensuring efficient processing of large and multimodal datasets. Its user-friendly frontend supports seamless data upload and interaction.
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            <a href="https://www.alphaxiv.org/abs/2512.01354v1" target="_blank" rel="noopener noreferrer">
                不完美之必要性：通过模拟认知有限性逆转模型崩溃
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            The Necessity of Imperfection:Reversing Model Collapse via Simulating Cognitive Boundedness
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhongjie Jiang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及模型崩溃和认知有限性，这属于理论性讨论，没有明确指向推荐系统、搜索或广告领域的实际应用。虽然可能涉及模型训练或泛化问题，但缺乏对Transformer架构、LLM技术或RecSys/Search/Ads具体应用的直接关联，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 07:09:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01354v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01354v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span><span class="category-tag">cs.LG</span><span class="category-tag">q-fin.TR</span></div>
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                    Although synthetic data is widely promoted as a remedy, its prevailing production paradigm -- one optimizing for statistical smoothness -- systematically removes the long-tail, cognitively grounded irregularities that characterize human text. Prolonged training on such statistically optimal but cognitively impoverished data accelerates model collapse. This paper proposes a paradigm shift: instead of imitating the surface properties of data, we simulate the cognitive processes that generate human text. We introduce the Prompt-driven Cognitive Computing Framework (PMCSF), whose core consists of a Cognitive State Decoder (CSD) that reverse-engineers unstructured text into structured cognitive vectors, and a Cognitive Text Encoder (CTE) that re-materializes these states into text enriched with human-typical imperfections via mathematically defined Cognitive Perturbation Operators. The framework is validated through a two-stage objective evaluation pipeline. First, in cognitive codec verification, CTE text yields a Jensen-Shannon divergence of 0.0614 from human text (vs. 0.4431 for standard LLM output), passes double-blind professional media review, and achieves an intraclass correlation coefficient ICC > 0.9 for cognitive profile alignment across heterogeneous models. Second, in functional gain evaluation, isomorphic stress tests in the A-share market show that strategies incorporating CTE-generated data reduce maximum drawdown by 47.4% during the 2015 crash and deliver 8.6% Defensive Alpha, exceeding transaction costs by a factor of 33. Our findings demonstrate that modelling human cognitive limitations -- not copying surface data -- enables synthetic data with genuine functional gain, offering a viable technical pathway toward resolving the AI data-collapse crisis.
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            <a href="https://www.alphaxiv.org/abs/2512.01335v1" target="_blank" rel="noopener noreferrer">
                EmoRAG：评估检索增强生成对符号扰动的鲁棒性
            </a>
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            EmoRAG: Evaluating RAG Robustness to Symbolic Perturbations
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinyun Zhou, Xinfeng Li, Yinan Peng, Ming Xu, Xuanwang Zhang, Miao Yu, Yidong Wa...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于RAG系统的评估和鲁棒性测试，属于纯粹的NLP评估基准研究。虽然RAG技术可能应用于搜索系统，但论文明确关注的是符号扰动下的评估方法，而非RAG在搜索/推荐中的实际应用或架构改进。这属于您列出的不相关主题中的'Evaluation benchmarks, or other purely NLP-centric topics'。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:53:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01335v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01335v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-Augmented Generation (RAG) systems are increasingly central to robust AI, enhancing large language model (LLM) faithfulness by incorporating external knowledge. However, our study unveils a critical, overlooked vulnerability: their profound susceptibility to subtle symbolic perturbations, particularly through near-imperceptible emoticon tokens such as "(@_@)" that can catastrophically mislead retrieval, termed EmoRAG. We demonstrate that injecting a single emoticon into a query makes it nearly 100% likely to retrieve semantically unrelated texts that contain a matching emoticon. Our extensive experiment across general question-answering and code domains, using a range of state-of-the-art retrievers and generators, reveals three key findings: (I) Single-Emoticon Disaster: Minimal emoticon injections cause maximal disruptions, with a single emoticon almost 100% dominating RAG output. (II) Positional Sensitivity: Placing an emoticon at the beginning of a query can cause severe perturbation, with F1-Scores exceeding 0.92 across all datasets. (III) Parameter-Scale Vulnerability: Counterintuitively, models with larger parameters exhibit greater vulnerability to the interference. We provide an in-depth analysis to uncover the underlying mechanisms of these phenomena. Furthermore, we raise a critical concern regarding the robustness assumption of current RAG systems, envisioning a threat scenario where an adversary exploits this vulnerability to manipulate the RAG system. We evaluate standard defenses and find them insufficient against EmoRAG. To address this, we propose targeted defenses, analyzing their strengths and limitations in mitigating emoticon-based perturbations. Finally, we outline future directions for building robust RAG systems.
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            <a href="https://www.alphaxiv.org/abs/2512.01242v1" target="_blank" rel="noopener noreferrer">
                用于抽象视觉组合生成的生成对抗Gumbel蒙特卡洛树搜索
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        <div class="mb-2 text-base text-gray-700">
            Generative Adversarial Gumbel MCTS for Abstract Visual Composition Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zirui Zhao, Boye Niu, David Hsu, Wee Sun Lee
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉组合生成，属于计算机视觉领域，与推荐系统、搜索或广告的核心排名任务没有直接关联。虽然生成对抗网络和蒙特卡洛树搜索是通用技术，但论文标题明确指向“抽象视觉组合生成”，这更接近AIGC或内容生成领域，属于明确排除的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 03:38:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01242v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01242v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    We study abstract visual composition, in which identity is primarily determined by the spatial configuration and relations among a small set of geometric primitives (e.g., parts, symmetry, topology). They are invariant primarily to texture and photorealistic detail. Composing such structures from fixed components under geometric constraints and vague goal specification (such as text) is non-trivial due to combinatorial placement choices, limited data, and discrete feasibility (overlap-free, allowable orientations), which create a sparse solution manifold ill-suited to purely statistical pixel-space generators. We propose a constraint-guided framework that combines explicit geometric reasoning with neural semantics. An AlphaGo-style search enforces feasibility, while a fine-tuned vision-language model scores semantic alignment as reward signals. Our algorithm uses a policy network as a heuristic in Monte-Carlo Tree Search and fine-tunes the network via search-generated plans. Inspired by the Generative Adversarial Network, we use the generated instances for adversarial reward refinement. Over time, the generation should approach the actual data more closely when the reward model cannot distinguish between generated instances and ground-truth. In the Tangram Assembly task, our approach yields higher validity and semantic fidelity than diffusion and auto-regressive baselines, especially as constraints tighten.
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            <a href="https://www.alphaxiv.org/abs/2512.01208v1" target="_blank" rel="noopener noreferrer">
                稍后关注：从向量空间扩散到线性对数谱相位锁定
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Pay Attention Later: From Vector Space Diffusion to Linearithmic Spectral Phase-Locking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alper Yıldırım, İbrahim Yücedağ
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题涉及向量空间扩散和谱相位锁定，可能属于Transformer架构中的注意力机制或效率改进领域。然而，标题过于抽象，缺乏明确的上下文，难以判断其具体技术内容或与推荐系统、搜索、广告的直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 02:46:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01208v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01208v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Standard Transformers suffer from a "Semantic Alignment Tax", a prohibitive optimization cost required to organize a chaotic initialization into a coherent geometric map via local gradient diffusion. We hypothesize that this reliance on diffusive learning creates "Catastrophic Rigidity", rendering models unable to adapt to novel concepts without destroying their pre-trained reasoning capabilities. To isolate this phenomenon, we introduce Iterative Semantic Map Refinement (ISMR), a diagnostic protocol revealing that alignment is a fixed geometric barrier that scaling cannot solve; a 20-layer model overcomes this barrier no faster than a 1-layer model. We introduce the Phase-Resonant Intelligent Spectral Model (PRISM). PRISM encodes semantic identity as resonant frequencies in the complex domain (C^d) and replaces quadratic self-attention with linearithmic O(N log N) Gated Harmonic Convolutions. We validate PRISM on the WMT14 translation task. While the Standard Transformer maintains a slight edge in general competence on static benchmarks (23.88 vs 21.40 BLEU), it fails the "Plasticity-Stability" stress test completely. When injected with novel concepts, the Transformer suffers Catastrophic Forgetting, degrading by -10.55 BLEU points while achieving only 60% acquisition. In contrast, PRISM demonstrates Lossless Plasticity, achieving 96% 5-shot acquisition with negligible degradation (-0.84 BLEU). These results suggest that harmonic representations effectively decouple memory from reasoning, offering a structural solution to the plasticity-stability dilemma in real-time knowledge adaptation.
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            <a href="https://www.alphaxiv.org/abs/2512.01183v1" target="_blank" rel="noopener noreferrer">
                TempPerturb-Eval：关于内部温度与外部扰动在RAG鲁棒性中的联合效应
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            TempPerturb-Eval: On the Joint Effects of Internal Temperature and External Perturbations in RAG Robustness
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yongxin Zhou, Philippe Mulhem, Didier Schwab
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题关注检索增强生成（RAG）系统的鲁棒性评估，属于纯粹的LLM评估范畴，与推荐系统、搜索或广告的核心技术进展无关。虽然RAG技术可能间接应用于搜索系统，但该论文聚焦于评估方法而非直接的技术应用或架构创新，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 01:46:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01183v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01183v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The evaluation of Retrieval-Augmented Generation (RAG) systems typically examines retrieval quality and generation parameters like temperature in isolation, overlooking their interaction. This work presents a systematic investigation of how text perturbations (simulating noisy retrieval) interact with temperature settings across multiple LLM runs. We propose a comprehensive RAG Perturbation-Temperature Analysis Framework that subjects retrieved documents to three distinct perturbation types across varying temperature settings. Through extensive experiments on HotpotQA with both open-source and proprietary LLMs, we demonstrate that performance degradation follows distinct patterns: high-temperature settings consistently amplify vulnerability to perturbations, while certain perturbation types exhibit non-linear sensitivity across the temperature range. Our work yields three key contributions: (1) a diagnostic benchmark for assessing RAG robustness, (2) an analytical framework for quantifying perturbation-temperature interactions, and (3) practical guidelines for model selection and parameter tuning under noisy retrieval conditions.
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            <a href="https://www.alphaxiv.org/abs/2512.01174v1" target="_blank" rel="noopener noreferrer">
                DrawingBench：通过基于鼠标的绘图任务评估大语言模型的空间推理和用户界面交互能力
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            DrawingBench: Evaluating Spatial Reasoning and UI Interaction Capabilities of Large Language Models through Mouse-Based Drawing Tasks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hyunjun Kim, Sooyoung Ryu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在空间推理和UI交互方面的评估基准，属于纯粹的LLM能力评估范畴，与RecSys/Search/Ads的核心技术进展或应用无直接关联。虽然UI交互能力可能在某些广告或推荐界面中有潜在应用，但论文焦点是评估而非应用，且未明确指向特定领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 01:18:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01174v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01174v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As agentic AI systems increasingly operate autonomously, establishing trust through verifiable evaluation becomes critical. Yet existing benchmarks lack the transparency and auditability needed to assess whether agents behave reliably. We present DrawingBench, a verification framework for evaluating the trustworthiness of agentic LLMs through spatial reasoning tasks that require generating sequences of low-level GUI actions. Unlike opaque evaluations, DrawingBench provides transparent, rule-based assessment: 8 objective criteria enable reproducible scoring, while action-level inspection allows stakeholders to audit agent behavior. Our framework comprises 250 diverse prompts across 20 categories and 4 difficulty levels, deterministic evaluation metrics, and an external oversight mechanism through multi-turn feedback that enables human control over agent refinement. Evaluating four state-of-the-art LLMs (Claude-4 Sonnet, GPT-4.1, GPT-4.1-mini, Gemini-2.5 Flash) across 1,000 tests, we establish both capabilities and limitations: models achieved 92.8% perfect performance with structured external feedback driving significant improvements (average +3.2%, up to +32.8% for complex scenes), but systematic error patterns emerged in tool state management and long-horizon planning. Notably, specification clarity proved more important than task complexity -- models achieved 100% perfect performance when given explicit, verifiable criteria. These findings demonstrate that transparent evaluation frameworks can establish trust in agentic systems, with external oversight proving more reliable than self-correction for guiding agent behavior. Our open-source framework provides a template for trustworthy agent assessment. Code and data: https://github.com/hyunjun1121/DrawingBench
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            <a href="https://www.alphaxiv.org/abs/2512.02012v1" target="_blank" rel="noopener noreferrer">
                改进的均值流：关于快速前向生成模型的挑战
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            <i class="fa fa-star mr-1"></i>2/10
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            Improved Mean Flows: On the Challenges of Fastforward Generative Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhengyang Geng, Yiyang Lu, Zongze Wu, Eli Shechtman, J. Zico Kolter, Kaiming He
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文讨论生成模型中的均值流改进，属于生成模型技术领域。虽然生成模型可能间接应用于推荐或广告的内容生成，但论文标题没有明确指向推荐系统、搜索或广告的具体应用，也没有涉及Transformer架构、LLM技术或异构数据统一建模等当前关注的核心方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:59:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02012v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02012v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    MeanFlow (MF) has recently been established as a framework for one-step generative modeling. However, its ``fastforward'' nature introduces key challenges in both the training objective and the guidance mechanism. First, the original MF's training target depends not only on the underlying ground-truth fields but also on the network itself. To address this issue, we recast the objective as a loss on the instantaneous velocity $v$, re-parameterized by a network that predicts the average velocity $u$. Our reformulation yields a more standard regression problem and improves the training stability. Second, the original MF fixes the classifier-free guidance scale during training, which sacrifices flexibility. We tackle this issue by formulating guidance as explicit conditioning variables, thereby retaining flexibility at test time. The diverse conditions are processed through in-context conditioning, which reduces model size and benefits performance. Overall, our $\textbf{improved MeanFlow}$ ($\textbf{iMF}$) method, trained entirely from scratch, achieves $\textbf{1.72}$ FID with a single function evaluation (1-NFE) on ImageNet 256$\times$256. iMF substantially outperforms prior methods of this kind and closes the gap with multi-step methods while using no distillation. We hope our work will further advance fastforward generative modeling as a stand-alone paradigm.
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            <a href="https://www.alphaxiv.org/abs/2512.01952v1" target="_blank" rel="noopener noreferrer">
                GrndCtrl：通过自监督奖励对齐实现世界模型的接地
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            GrndCtrl: Grounding World Models via Self-Supervised Reward Alignment
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haoyang He, Jay Patrikar, Dong-Ki Kim, Max Smith, Daniel McGann, Ali-akbar Agha-...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及世界模型和奖励对齐，主要属于强化学习领域。虽然提到了自监督学习，但没有明确展示与推荐系统、搜索或广告的直接关联。标题未提及任何具体的推荐、搜索或广告应用场景，也未涉及LLM、Transformer架构或异构数据处理等当前关注的核心领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:03:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01952v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01952v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.RO</span></div>
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                    Recent advances in video world modeling have enabled large-scale generative models to simulate embodied environments with high visual fidelity, providing strong priors for prediction, planning, and control. Yet, despite their realism, these models often lack geometric grounding, limiting their use in navigation tasks that require spatial coherence and long-horizon stability. We introduce Reinforcement Learning with World Grounding (RLWG), a self-supervised post-training framework that aligns pretrained world models with a physically verifiable structure through geometric and perceptual rewards. Analogous to reinforcement learning from verifiable feedback (RLVR) in language models, RLWG can use multiple rewards that measure pose cycle-consistency, depth reprojection, and temporal coherence. We instantiate this framework with GrndCtrl, a reward-aligned adaptation method based on Group Relative Policy Optimization (GRPO), yielding world models that maintain stable trajectories, consistent geometry, and reliable rollouts for embodied navigation. Like post-training alignment in large language models, GrndCtrl leverages verifiable rewards to bridge generative pretraining and grounded behavior, achieving superior spatial coherence and navigation stability over supervised fine-tuning in outdoor environments.
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            <a href="https://www.alphaxiv.org/abs/2512.01908v1" target="_blank" rel="noopener noreferrer">
                SARL：用于视觉-触觉感知的空间感知自监督表征学习
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            SARL: Spatially-Aware Self-Supervised Representation Learning for Visuo-Tactile Perception
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gurmeher Khurana, Lan Wei, Dandan Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及多模态感知（视觉-触觉），这与VLM处理异构数据的类比有一定关联，但其核心是机器人或物理交互中的感知任务，而非推荐、搜索或广告中的异构数据建模。尽管自监督学习是通用技术，但论文未明确展示其在推荐系统、搜索或广告领域的潜在应用，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:26:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01908v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01908v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Contact-rich robotic manipulation requires representations that encode local geometry. Vision provides global context but lacks direct measurements of properties such as texture and hardness, whereas touch supplies these cues. Modern visuo-tactile sensors capture both modalities in a single fused image, yielding intrinsically aligned inputs that are well suited to manipulation tasks requiring visual and tactile information. Most self-supervised learning (SSL) frameworks, however, compress feature maps into a global vector, discarding spatial structure and misaligning with the needs of manipulation. To address this, we propose SARL, a spatially-aware SSL framework that augments the Bootstrap Your Own Latent (BYOL) architecture with three map-level objectives, including Saliency Alignment (SAL), Patch-Prototype Distribution Alignment (PPDA), and Region Affinity Matching (RAM), to keep attentional focus, part composition, and geometric relations consistent across views. These losses act on intermediate feature maps, complementing the global objective. SARL consistently outperforms nine SSL baselines across six downstream tasks with fused visual-tactile data. On the geometry-sensitive edge-pose regression task, SARL achieves a Mean Absolute Error (MAE) of 0.3955, a 30% relative improvement over the next-best SSL method (0.5682 MAE) and approaching the supervised upper bound. These findings indicate that, for fused visual-tactile data, the most effective signal is structured spatial equivariance, in which features vary predictably with object geometry, which enables more capable robotic perception.
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            <a href="https://www.alphaxiv.org/abs/2512.01853v1" target="_blank" rel="noopener noreferrer">
                COACH：用于上下文高亮的协作智能体——一个用于体育视频分析的多智能体框架
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            COACH: Collaborative Agents for Contextual Highlighting - A Multi-Agent Framework for Sports Video Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tsz-To Wong, Ching-Chun Huang, Hong-Han Shuai
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注体育视频分析的多智能体框架，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术焦点没有直接关联。虽然多智能体协作可能启发某些分布式系统设计，但论文标题明确指向特定领域应用（体育视频），缺乏明确的跨模态建模或推荐/搜索应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 16:38:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01853v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01853v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Intelligent sports video analysis demands a comprehensive understanding of temporal context, from micro-level actions to macro-level game strategies. Existing end-to-end models often struggle with this temporal hierarchy, offering solutions that lack generalization, incur high development costs for new tasks, and suffer from poor interpretability. To overcome these limitations, we propose a reconfigurable Multi-Agent System (MAS) as a foundational framework for sports video understanding. In our system, each agent functions as a distinct "cognitive tool" specializing in a specific aspect of analysis. The system's architecture is not confined to a single temporal dimension or task. By leveraging iterative invocation and flexible composition of these agents, our framework can construct adaptive pipelines for both short-term analytic reasoning (e.g., Rally QA) and long-term generative summarization (e.g., match summaries). We demonstrate the adaptability of this framework using two representative tasks in badminton analysis, showcasing its ability to bridge fine-grained event detection and global semantic organization. This work presents a paradigm shift towards a flexible, scalable, and interpretable system for robust, cross-task sports video intelligence.The project homepage is available at https://aiden1020.github.io/COACH-project-page
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            <a href="https://www.alphaxiv.org/abs/2512.01827v1" target="_blank" rel="noopener noreferrer">
                CauSight：学习超感知以实现视觉因果发现
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            CauSight: Learning to Supersense for Visual Causal Discovery
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yize Zhang, Meiqi Chen, Sirui Chen, Bo Peng, Yanxi Zhang, Tianyu Li, Chaochao Lu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于计算机视觉领域的因果发现，属于视觉因果推理范畴。虽然因果推理在推荐/搜索系统中可能有潜在应用（如理解用户行为因果关系），但该论文明确限定于视觉模态，且未提及任何与推荐系统、搜索或广告相关的应用场景，因此与您关注的领域相关性较弱。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 16:05:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01827v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01827v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Causal thinking enables humans to understand not just what is seen, but why it happens. To replicate this capability in modern AI systems, we introduce the task of visual causal discovery. It requires models to infer cause-and-effect relations among visual entities across diverse scenarios instead of merely perceiving their presence. To this end, we first construct the Visual Causal Graph dataset (VCG-32K), a large-scale collection of over 32,000 images annotated with entity-level causal graphs, and further develop CauSight, a novel vision-language model to perform visual causal discovery through causally aware reasoning. Our training recipe integrates three components: (1) training data curation from VCG-32K, (2) Tree-of-Causal-Thought (ToCT) for synthesizing reasoning trajectories, and (3) reinforcement learning with a designed causal reward to refine the reasoning policy. Experiments show that CauSight outperforms GPT-4.1 on visual causal discovery, achieving over a threefold performance boost (21% absolute gain). Our code, model, and dataset are fully open-sourced at project page: https://github.com/OpenCausaLab/CauSight.
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            <a href="https://www.alphaxiv.org/abs/2512.01821v1" target="_blank" rel="noopener noreferrer">
                透过想象看世界：通过隐式空间世界建模学习场景几何
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            Seeing through Imagination: Learning Scene Geometry via Implicit Spatial World Modeling
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Meng Cao, Haokun Lin, Haoyuan Li, Haoran Tang, Rongtao Xu, Dong An, Xue Liu, Ian...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的场景几何理解，属于纯粹的视觉领域研究。虽然标题提到“世界建模”，但这更多指向3D场景重建而非推荐/搜索/广告系统。论文没有明确展示与异构数据处理、Transformer架构或LLM应用的直接联系，因此与您关注的领域相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 16:01:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01821v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01821v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Spatial reasoning, the ability to understand and interpret the 3D structure of the world, is a critical yet underdeveloped capability in Multimodal Large Language Models (MLLMs). Current methods predominantly rely on verbal descriptive tuning, which suffers from visual illiteracy, i.e., they learn spatial concepts through textual symbols alone, devoid of connection to their visual manifestations. To bridge this gap, this paper introduces MILO, an Implicit spatIaL wOrld modeling paradigm that simulates human-like spatial imagination. MILO integrates a visual generator to provide geometry-aware feedback, thereby implicitly grounding the MLLM's symbolic reasoning in perceptual experience. Complementing this paradigm, we propose RePE (Relative Positional Encoding), a novel encoding scheme that captures relative camera-pose transformations, offering superior performance over absolute coordinate systems. To support the training, we construct GeoGen, a large-scale Geometry-aware Generative dataset with approximately 2,241 videos and 67,827 observation-action-outcome triplets. Experiments demonstrate that our approach significantly enhances spatial reasoning capabilities across multiple baselines and benchmarks, offering a more holistic understanding of 3D space.
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                遗忘更少，保留更多：一种用于基于排练的持续学习的轻量级正则化器
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            Forget Less, Retain More: A Lightweight Regularizer for Rehearsal-Based Continual Learning
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lama Alssum, Hasan Abed Al Kader Hammoud, Motasem Alfarra, Juan C Leon Alcazar, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注持续学习中的正则化技术，属于通用机器学习方法。虽然持续学习在推荐系统中可能有应用（如处理用户兴趣漂移），但论文标题没有明确指向RecSys/Search/Ads领域，也没有涉及LLM、Transformer架构或VLM类比等当前关注的核心技术。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 15:56:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01818v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01818v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Deep neural networks suffer from catastrophic forgetting, where performance on previous tasks degrades after training on a new task. This issue arises due to the model's tendency to overwrite previously acquired knowledge with new information. We present a novel approach to address this challenge, focusing on the intersection of memory-based methods and regularization approaches. We formulate a regularization strategy, termed Information Maximization (IM) regularizer, for memory-based continual learning methods, which is based exclusively on the expected label distribution, thus making it class-agnostic. As a consequence, IM regularizer can be directly integrated into various rehearsal-based continual learning methods, reducing forgetting and favoring faster convergence. Our empirical validation shows that, across datasets and regardless of the number of tasks, our proposed regularization strategy consistently improves baseline performance at the expense of a minimal computational overhead. The lightweight nature of IM ensures that it remains a practical and scalable solution, making it applicable to real-world continual learning scenarios where efficiency is paramount. Finally, we demonstrate the data-agnostic nature of our regularizer by applying it to video data, which presents additional challenges due to its temporal structure and higher memory requirements. Despite the significant domain gap, our experiments show that IM regularizer also improves the performance of video continual learning methods.
                </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01763v1" target="_blank" rel="noopener noreferrer">
                HiconAgent：面向图形用户界面代理的历史上下文感知策略优化
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            HiconAgent: History Context-aware Policy Optimization for GUI Agents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xurui Zhou, Gongwei Chen, Yuquan Xie, Zaijing Li, Kaiwen Zhou, Shuai Wang, Shuo ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于GUI代理的策略优化，属于特定应用领域的智能体研究，与推荐系统、搜索或广告的核心技术无直接关联。虽然涉及上下文感知，但未明确指向推荐/搜索/广告领域的异构数据建模或LLM应用场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 15:06:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01763v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01763v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Graphical User Interface (GUI) agents require effective use of historical context to perform sequential navigation tasks. While incorporating past actions and observations can improve decision making, naive use of full history leads to excessive computational overhead and distraction from irrelevant information. To address this, we introduce HiconAgent, a GUI agent trained with History Context-aware Policy Optimization (HCPO) for efficient and effective utilization of historical information. HCPO optimizes history usage in both sampling and policy updates through two complementary components: (1) Dynamic Context Sampling (DCS) presents the agent with variable length histories during sampling, enabling adaptive use of the most relevant context; (2) Anchor-guided History Compression (AHC) refines the policy update phase with a dual branch strategy where the compressed branch removes history observations while keeping history actions as information flow anchors. The compressed and uncompressed branches are coupled through a history-enhanced alignment loss to enforce consistent history usage while maintaining efficiency. Experiments on mainstream GUI navigation benchmarks demonstrate strong performance. Despite being smaller, HiconAgent-3B outperforms GUI-R1-7B by +8.46 percent grounding accuracy and +11.32 percent step success rate on GUI-Odyssey, while achieving comparable results on AndroidControl and AITW with up to 2.47x computational speedup and 60 percent FLOPs reduction.
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 * @Author: Doragd doragd@users.noreply.github.com
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01701v1" target="_blank" rel="noopener noreferrer">
                SSR：基于CLIP的弱监督分割的语义与空间校正
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            SSR: Semantic and Spatial Rectification for CLIP-based Weakly Supervised Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiuli Bi, Die Xiao, Junchao Fan, Bin Xiao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的弱监督分割任务，虽然使用了CLIP模型，但其核心是视觉分割而非推荐/搜索/广告应用。论文的语义和空间校正技术可能对多模态理解有启发，但缺乏明确的推荐/搜索/广告应用场景或直接相关性。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 14:06:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01701v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01701v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In recent years, Contrastive Language-Image Pretraining (CLIP) has been widely applied to Weakly Supervised Semantic Segmentation (WSSS) tasks due to its powerful cross-modal semantic understanding capabilities. This paper proposes a novel Semantic and Spatial Rectification (SSR) method to address the limitations of existing CLIP-based weakly supervised semantic segmentation approaches: over-activation in non-target foreground regions and background areas. Specifically, at the semantic level, the Cross-Modal Prototype Alignment (CMPA) establishes a contrastive learning mechanism to enforce feature space alignment across modalities, reducing inter-class overlap while enhancing semantic correlations, to rectify over-activation in non-target foreground regions effectively; at the spatial level, the Superpixel-Guided Correction (SGC) leverages superpixel-based spatial priors to precisely filter out interference from non-target regions during affinity propagation, significantly rectifying background over-activation. Extensive experiments on the PASCAL VOC and MS COCO datasets demonstrate that our method outperforms all single-stage approaches, as well as more complex multi-stage approaches, achieving mIoU scores of 79.5% and 50.6%, respectively.
                </div>
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 * @Date: 2025-10-09 23:23:38
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01686v1" target="_blank" rel="noopener noreferrer">
                DreamingComics：通过基于视频模型的主体与布局定制化生成的故事可视化流水线
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            DreamingComics: A Story Visualization Pipeline via Subject and Layout Customized Generation using Video Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Patrick Kwon, Chen Chen
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注故事可视化流水线和视频生成模型，属于内容生成领域。虽然标题中提到“定制化生成”，但这更偏向AIGC和视觉内容生成，与推荐系统、搜索或广告中的核心排名、检索、架构优化或异构数据建模等焦点领域没有直接关联。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 13:51:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01686v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01686v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Current story visualization methods tend to position subjects solely by text and face challenges in maintaining artistic consistency. To address these limitations, we introduce DreamingComics, a layout-aware story visualization framework. We build upon a pretrained video diffusion-transformer (DiT) model, leveraging its spatiotemporal priors to enhance identity and style consistency. For layout-based position control, we propose RegionalRoPE, a region-aware positional encoding scheme that re-indexes embeddings based on the target layout. Additionally, we introduce a masked condition loss to further constrain each subject's visual features to their designated region. To infer layouts from natural language scripts, we integrate an LLM-based layout generator trained to produce comic-style layouts, enabling flexible and controllable layout conditioning. We present a comprehensive evaluation of our approach, showing a 29.2% increase in character consistency and a 36.2% increase in style similarity compared to previous methods, while displaying high spatial accuracy. Our project page is available at https://yj7082126.github.io/dreamingcomics/
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01629v1" target="_blank" rel="noopener noreferrer">
                SPARK：基于视觉语言模型知识的仿真就绪部件级关节重建
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            SPARK: Sim-ready Part-level Articulated Reconstruction with VLM Knowledge
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yumeng He, Ying Jiang, Jiayin Lu, Yin Yang, Chenfanfu Jiang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及视觉语言模型（VLM）和3D重建技术，但主要聚焦于部件级关节重建和仿真准备，这属于计算机视觉和图形学领域。虽然提到了VLM知识，但缺乏与推荐系统、搜索或广告领域的明确连接点，且3D重建本身不在您的关注范围内。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 12:51:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01629v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01629v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Articulated 3D objects are critical for embodied AI, robotics, and interactive scene understanding, yet creating simulation-ready assets remains labor-intensive and requires expert modeling of part hierarchies and motion structures. We introduce SPARK, a framework for reconstructing physically consistent, kinematic part-level articulated objects from a single RGB image. Given an input image, we first leverage VLMs to extract coarse URDF parameters and generate part-level reference images. We then integrate the part-image guidance and the inferred structure graph into a generative diffusion transformer to synthesize consistent part and complete shapes of articulated objects. To further refine the URDF parameters, we incorporate differentiable forward kinematics and differentiable rendering to optimize joint types, axes, and origins under VLM-generated open-state supervision. Extensive experiments show that SPARK produces high-quality, simulation-ready articulated assets across diverse categories, enabling downstream applications such as robotic manipulation and interaction modeling.
                </div>
            </details>
    </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01533v1" target="_blank" rel="noopener noreferrer">
                扩散模糊系统：模糊规则引导的潜在多路径扩散建模
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Diffusion Fuzzy System: Fuzzy Rule Guided Latent Multi-Path Diffusion Modeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hailong Yang, Te Zhang, Kup-sze Choi, Zhaohong Deng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要涉及扩散模型与模糊系统的结合，属于生成模型的理论改进。虽然扩散模型在生成任务中表现出色，但论文未明确展示其在推荐系统、搜索或广告中的直接应用潜力。模糊系统通常用于不确定性建模，但该工作更偏向于理论架构创新，缺乏明确的推荐/搜索/广告应用场景说明。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 11:01:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01533v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01533v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Diffusion models have emerged as a leading technique for generating images due to their ability to create high-resolution and realistic images. Despite their strong performance, diffusion models still struggle in managing image collections with significant feature differences. They often fail to capture complex features and produce conflicting results. Research has attempted to address this issue by learning different regions of an image through multiple diffusion paths and then combining them. However, this approach leads to inefficient coordination among multiple paths and high computational costs. To tackle these issues, this paper presents a Diffusion Fuzzy System (DFS), a latent-space multi-path diffusion model guided by fuzzy rules. DFS offers several advantages. First, unlike traditional multi-path diffusion methods, DFS uses multiple diffusion paths, each dedicated to learning a specific class of image features. By assigning each path to a different feature type, DFS overcomes the limitations of multi-path models in capturing heterogeneous image features. Second, DFS employs rule-chain-based reasoning to dynamically steer the diffusion process and enable efficient coordination among multiple paths. Finally, DFS introduces a fuzzy membership-based latent-space compression mechanism to reduce the computational costs of multi-path diffusion effectively. We tested our method on three public datasets: LSUN Bedroom, LSUN Church, and MS COCO. The results show that DFS achieves more stable training and faster convergence than existing single-path and multi-path diffusion models. Additionally, DFS surpasses baseline models in both image quality and alignment between text and images, and also shows improved accuracy when comparing generated images to target references.
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            <a href="https://www.alphaxiv.org/abs/2512.01427v1" target="_blank" rel="noopener noreferrer">
                语言引导的开放世界异常分割
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Language-Guided Open-World Anomaly Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Klara Reichard, Nikolas Brasch, Nassir Navab, Federico Tombari
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的异常分割任务，虽然涉及语言引导，但核心是视觉异常检测而非推荐系统、搜索或广告的直接应用。开放世界异常分割与推荐/搜索广告中的异构数据处理没有明显关联，且缺乏明确的跨模态建模或Transformer架构创新。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 09:08:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01427v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01427v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Open-world and anomaly segmentation methods seek to enable autonomous driving systems to detect and segment both known and unknown objects in real-world scenes. However, existing methods do not assign semantically meaningful labels to unknown regions, and distinguishing and learning representations for unknown classes remains difficult. While open-vocabulary segmentation methods show promise in generalizing to novel classes, they require a fixed inference vocabulary and thus cannot be directly applied to anomaly segmentation where unknown classes are unconstrained. We propose Clipomaly, the first CLIP-based open-world and anomaly segmentation method for autonomous driving. Our zero-shot approach requires no anomaly-specific training data and leverages CLIP's shared image-text embedding space to both segment unknown objects and assign human-interpretable names to them. Unlike open-vocabulary methods, our model dynamically extends its vocabulary at inference time without retraining, enabling robust detection and naming of anomalies beyond common class definitions such as those in Cityscapes. Clipomaly achieves state-of-the-art performance on established anomaly segmentation benchmarks while providing interpretability and flexibility essential for practical deployment.
                </div>
            </details>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01426v1" target="_blank" rel="noopener noreferrer">
                ResDiT：激发扩散变换器中的固有分辨率可扩展性
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            ResDiT: Evoking the Intrinsic Resolution Scalability in Diffusion Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiyang Ma, Feng Zhou, Xuedan Yin, Pu Cao, Yonghao Dang, Jianqin Yin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注扩散模型和变换器架构的效率改进，属于“使能变换器技术”范畴。虽然分辨率可扩展性可能对某些推荐/广告中的图像处理有间接价值，但论文标题明确聚焦于扩散变换器，这与推荐系统、搜索或广告的核心排名任务没有直接关联，且扩散模型主要属于AIGC/内容生成领域。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 09:08:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01426v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01426v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Leveraging pre-trained Diffusion Transformers (DiTs) for high-resolution (HR) image synthesis often leads to spatial layout collapse and degraded texture fidelity. Prior work mitigates these issues with complex pipelines that first perform a base-resolution (i.e., training-resolution) denoising process to guide HR generation. We instead explore the intrinsic generative mechanisms of DiTs and propose ResDiT, a training-free method that scales resolution efficiently. We identify the core factor governing spatial layout, position embeddings (PEs), and show that the original PEs encode incorrect positional information when extrapolated to HR, which triggers layout collapse. To address this, we introduce a PE scaling technique that rectifies positional encoding under resolution changes. To further remedy low-fidelity details, we develop a local-enhancement mechanism grounded in base-resolution local attention. We design a patch-level fusion module that aggregates global and local cues, together with a Gaussian-weighted splicing strategy that eliminates grid artifacts. Comprehensive evaluations demonstrate that ResDiT consistently delivers high-fidelity, high-resolution image synthesis and integrates seamlessly with downstream tasks, including spatially controlled generation.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01419v1" target="_blank" rel="noopener noreferrer">
                Rice-VL：评估视觉语言模型在东盟国家间的文化理解能力
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            Rice-VL: Evaluating Vision-Language Models for Cultural Understanding Across ASEAN Countries
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tushar Pranav, Eshan Pandey, Austria Lyka Diane Bala, Aman Chadha, Indriyati Atm...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于视觉语言模型（VLM）在特定地理区域（东盟国家）的文化理解评估。虽然提到了VLM，但其核心是文化评估而非技术架构创新，且未建立与推荐系统、搜索或广告领域的明确联系。根据用户要求，应排除“纯粹视觉或语言模型论文而无明确RecSys/Search/Ads相关性”以及“评估基准”等主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 08:55:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01419v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01419v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision-Language Models (VLMs) excel in multimodal tasks but often exhibit Western-centric biases, limiting their effectiveness in culturally diverse regions like Southeast Asia (SEA). To address this, we introduce RICE-VL, a novel benchmark evaluating VLM cultural understanding across 11 ASEAN countries. RICE-VL includes over 28,000 human-curated Visual Question Answering (VQA) samples -- covering True or False, Fill-in-the-Blank, and open-ended formats -- and 1,000 image-bounding box pairs for Visual Grounding, annotated by culturally informed experts across 14 sub-ground categories. We propose SEA-LAVE, an extension of the LAVE metric, assessing textual accuracy, cultural alignment, and country identification. Evaluations of six open- and closed-source VLMs reveal significant performance gaps in low-resource countries and abstract cultural domains. The Visual Grounding task tests models' ability to localize culturally significant elements in complex scenes, probing spatial and contextual accuracy. RICE-VL exposes limitations in VLMs' cultural comprehension and highlights the need for inclusive model development to better serve diverse global populations.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01342v1" target="_blank" rel="noopener noreferrer">
                InternVideo-Next：迈向无需视频-文本监督的通用视频基础模型
            </a>
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        <div class="mb-2 text-base text-gray-700">
            InternVideo-Next: Towards General Video Foundation Models without Video-Text Supervision
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenting Wang, Yuhan Zhu, Yicheng Xu, Jiange Yang, Ziang Yan, Yali Wang, Yi Wang...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频基础模型，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术关联较弱。虽然视频理解在内容推荐中有潜在应用，但论文标题强调“无需视频-文本监督”，主要涉及视觉模态的预训练方法，未明确指向RecSys/Search/Ads的具体应用或技术交叉。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:57:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01342v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01342v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large-scale video-text pretraining achieves strong performance but depends on noisy, synthetic captions with limited semantic coverage, often overlooking implicit world knowledge such as object motion, 3D geometry, and physical cues. In contrast, masked video modeling (MVM) directly exploits spatiotemporal structures but trails text-supervised methods on general tasks. We find this gap arises from overlooked architectural issues: pixel-level reconstruction struggles with convergence and its low-level requirement often conflicts with semantics, while latent prediction often encourages shortcut learning. To address these, we disentangle the traditional encoder-decoder design into an Encoder-Predictor-Decoder (EPD) framework, where the predictor acts as a latent world model, and propose InternVideo-Next, a two-stage pretraining scheme that builds a semantically consistent yet detail-preserving latent space for this world model. First, conventional linear decoder in pixel MVM enforces the predictor output latent to be linearly projected to, thus separable in pixel space, causing the conflict with semantic abstraction. Our Stage 1 proposes a conditional diffusion decoder and injects reliable image-level semantic priors to enhance semantics and convergence, thus bridging pixel-level fidelity with high-level semantic abstraction. Stage 2 further learns world knowledge by predicting frozen Stage 1 targets within this space, mitigating shortcut learning. Trained on public, unlabeled videos, InternVideo-Next achieves state-of-the-art results across benchmarks and provides a scalable path toward general video representation learning.
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            <a href="https://www.alphaxiv.org/abs/2512.01302v1" target="_blank" rel="noopener noreferrer">
                DCText：基于分治策略的视觉文本生成中的调度注意力掩码
            </a>
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        <div class="mb-2 text-base text-gray-700">
            DCText: Scheduled Attention Masking for Visual Text Generation via Divide-and-Conquer Strategy
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jaewoo Song, Jooyoung Choi, Kanghyun Baek, Sangyub Lee, Daemin Park, Sungroh Yoo...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉文本生成，属于计算机视觉与自然语言处理的交叉领域，但标题中未提及任何与推荐系统、搜索或广告相关的应用或技术迁移潜力。虽然注意力机制是Transformer架构的核心组件，但论文聚焦于视觉文本生成这一特定任务，缺乏明确的RecSys/Search/Ads应用场景或技术启发性。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 05:52:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01302v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01302v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Despite recent text-to-image models achieving highfidelity text rendering, they still struggle with long or multiple texts due to diluted global attention. We propose DCText, a training-free visual text generation method that adopts a divide-and-conquer strategy, leveraging the reliable short-text generation of Multi-Modal Diffusion Transformers. Our method first decomposes a prompt by extracting and dividing the target text, then assigns each to a designated region. To accurately render each segment within their regions while preserving overall image coherence, we introduce two attention masks - Text-Focus and Context-Expansion - applied sequentially during denoising. Additionally, Localized Noise Initialization further improves text accuracy and region alignment without increasing computational cost. Extensive experiments on single- and multisentence benchmarks show that DCText achieves the best text accuracy without compromising image quality while also delivering the lowest generation latency.
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            <a href="https://www.alphaxiv.org/abs/2512.01298v1" target="_blank" rel="noopener noreferrer">
                TBT-Former：学习用于动作定位的时间边界分布
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        <div class="mb-2 text-base text-gray-700">
            TBT-Former: Learning Temporal Boundary Distributions for Action Localization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Thisara Rathnayaka, Uthayasanker Thayasivam
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频中的动作定位，属于计算机视觉领域，与推荐系统、搜索或广告的核心关注点没有直接关联。虽然时间建模技术在某些序列推荐场景中可能有间接参考价值，但论文标题明确指向视觉动作识别任务，缺乏明确的RecSys/Search/Ads应用潜力说明。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 05:38:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01298v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01298v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Temporal Action Localization (TAL) remains a fundamental challenge in video understanding, aiming to identify the start time, end time, and category of all action instances within untrimmed videos. While recent single-stage, anchor-free models like ActionFormer have set a high standard by leveraging Transformers for temporal reasoning, they often struggle with two persistent issues: the precise localization of actions with ambiguous or "fuzzy" temporal boundaries and the effective fusion of multi-scale contextual information. In this paper, we introduce the Temporal Boundary Transformer (TBT-Former), a new architecture that directly addresses these limitations. TBT-Former enhances the strong ActionFormer baseline with three core contributions: (1) a higher-capacity scaled Transformer backbone with an increased number of attention heads and an expanded Multi-Layer Perceptron (MLP) dimension for more powerful temporal feature extraction; (2) a cross-scale feature pyramid network (FPN) that integrates a top-down pathway with lateral connections, enabling richer fusion of high-level semantics and low-level temporal details; and (3) a novel boundary distribution regression head. Inspired by the principles of Generalized Focal Loss (GFL), this new head recasts the challenging task of boundary regression as a more flexible probability distribution learning problem, allowing the model to explicitly represent and reason about boundary uncertainty. Within the paradigm of Transformer-based architectures, TBT-Former advances the formidable benchmark set by its predecessors, establishing a new level of performance on the highly competitive THUMOS14 and EPIC-Kitchens 100 datasets, while remaining competitive on the large-scale ActivityNet-1.3. Our code is available at https://github.com/aaivu/In21-S7-CS4681-AML-Research-Projects/tree/main/projects/210536K-Multi-Modal-Learning_Video-Understanding
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            <a href="https://www.alphaxiv.org/abs/2512.01948v1" target="_blank" rel="noopener noreferrer">
                我们距离真正有用的深度研究智能体还有多远？
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        <div class="mb-2 text-base text-gray-700">
            How Far Are We from Genuinely Useful Deep Research Agents?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dingling Zhang, He Zhu, Jincheng Ren, Kangqi Song, Xinran Zhou, Boyu Feng, Shudo...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于研究智能体的通用能力评估，属于纯粹的LLM-centric主题，与推荐系统、搜索或广告领域的核心进展、使能技术或直接应用无关。标题中未提及任何与异构数据处理、Transformer架构改进或特定领域应用相关的内容。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:58:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01948v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01948v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Deep Research Agents (DRAs) aim to automatically produce analyst-level reports through iterative information retrieval and synthesis. However, most existing DRAs were validated on question-answering benchmarks, while research on generating comprehensive reports remains overlooked. Worse, current benchmarks for report synthesis suffer from task complexity and subjective metrics -- this fails to reflect user demands and limits the practical utility of generated reports. To address these gaps, we present Fine-grained DEepResearch bench (FINDER), an enhanced benchmark consisting of 100 human-curated research tasks with 419 structured checklist items that standardize report structure, analytical depth, and factual grounding. Based on approximately 1,000 reports produced by mainstream DRAs, we further propose Deep rEsearch Failure Taxonomy (DEFT), the first failure taxonomy for deep research agents. DEFT contains 14 fine-grained failure modes across reasoning, retrieval, and generation, and is built upon grounded theory with human-LLM co-annotating and inter-annotator reliability validation. Our experimental findings reveal that current DRAs struggle not with task comprehension but with evidence integration, verification, and reasoning-resilient planning.
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            <a href="https://www.alphaxiv.org/abs/2512.01892v1" target="_blank" rel="noopener noreferrer">
                探索人类对AI响应的感知：基于生成模型风险缓解的混合方法研究洞见
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            Exploring Human Perceptions of AI Responses: Insights from a Mixed-Methods Study on Risk Mitigation in Generative Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Heloisa Candello, Muneeza Azmat, Uma Sushmitha Gunturi, Raya Horesh, Rogerio Abr...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于人类对AI响应的感知研究，属于用户研究、人机交互或AI伦理评估范畴。虽然涉及生成模型，但核心是风险缓解和人类感知的混合方法研究，这直接落入被排除的“非技术性话题”（如伦理、公平性）和“纯粹LLM中心话题”（如评估基准），与当前关注的推荐系统/搜索/广告领域的技术架构、模型效率或直接应用无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:12:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01892v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01892v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.HC</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the rapid uptake of generative AI, investigating human perceptions of generated responses has become crucial. A major challenge is their `aptitude' for hallucinating and generating harmful contents. Despite major efforts for implementing guardrails, human perceptions of these mitigation strategies are largely unknown. We conducted a mixed-method experiment for evaluating the responses of a mitigation strategy across multiple-dimensions: faithfulness, fairness, harm-removal capacity, and relevance. In a within-subject study design, 57 participants assessed the responses under two conditions: harmful response plus its mitigation and solely mitigated response. Results revealed that participants' native language, AI work experience, and annotation familiarity significantly influenced evaluations. Participants showed high sensitivity to linguistic and contextual attributes, penalizing minor grammar errors while rewarding preserved semantic contexts. This contrasts with how language is often treated in the quantitative evaluation of LLMs. We also introduced new metrics for training and evaluating mitigation strategies and insights for human-AI evaluation studies.
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            <a href="https://www.alphaxiv.org/abs/2512.01852v1" target="_blank" rel="noopener noreferrer">
                BHRAM-IL：多印度语言幻觉识别与评估基准
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            BHRAM-IL: A Benchmark for Hallucination Recognition and Assessment in Multiple Indian Languages
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hrishikesh Terdalkar, Kirtan Bhojani, Aryan Dongare, Omm Aditya Behera
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及幻觉评估基准，属于'Irrelevant Topics'中明确排除的'Hallucination, Evaluation benchmarks, or other purely NLP-centric topics'范畴。虽然涉及多语言处理，但核心是幻觉评估这一纯NLP主题，与推荐系统、搜索或广告的技术进展、LLM应用或Transformer架构改进无直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 16:37:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01852v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01852v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.ET</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) are increasingly deployed in multilingual applications but often generate plausible yet incorrect or misleading outputs, known as hallucinations. While hallucination detection has been studied extensively in English, under-resourced Indian languages remain largely unexplored. We present BHRAM-IL, a benchmark for hallucination recognition and assessment in multiple Indian languages, covering Hindi, Gujarati, Marathi, Odia, along with English. The benchmark comprises 36,047 curated questions across nine categories spanning factual, numerical, reasoning, and linguistic tasks. We evaluate 14 state-of-the-art multilingual LLMs on a benchmark subset of 10,265 questions, analyzing cross-lingual and factual hallucinations across languages, models, scales, categories, and domains using category-specific metrics normalized to (0,1) range. Aggregation over all categories and models yields a primary score of 0.23 and a language-corrected fuzzy score of 0.385, demonstrating the usefulness of BHRAM-IL for hallucination-focused evaluation. The dataset, and the code for generation and evaluation are available on GitHub (https://github.com/sambhashana/BHRAM-IL/) and HuggingFace (https://huggingface.co/datasets/sambhashana/BHRAM-IL/) to support future research in multilingual hallucination detection and mitigation.
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            <a href="https://www.alphaxiv.org/abs/2512.01822v1" target="_blank" rel="noopener noreferrer">
                InnoGym：评估AI智能体的创新潜力基准
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            InnoGym: Benchmarking the Innovation Potential of AI Agents
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jintian Zhang, Kewei Xu, Jingsheng Zheng, Zhuoyun Yu, Yuqi Zhu, Yujie Luo, Lanni...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于AI智能体的创新潜力评估基准，属于通用AI智能体研究范畴。虽然AI智能体技术可能在某些场景下应用于推荐或搜索系统，但该标题本身并未明确指向推荐系统、搜索或广告领域的核心进展、使能技术或直接应用，也未涉及Transformer架构、多模态建模等具体技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 16:03:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01822v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01822v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.MA</span></div>
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                    LLMs and Agents have achieved impressive progress in code generation, mathematical reasoning, and scientific discovery. However, existing benchmarks primarily measure correctness, overlooking the diversity of methods behind solutions. True innovation depends not only on producing correct answers but also on the originality of the approach. We present InnoGym, the first benchmark and framework designed to systematically evaluate the innovation potential of AI agents. InnoGym introduces two complementary metrics: performance gain, which measures improvement over the best-known solutions, and novelty, which captures methodological differences from prior approaches. The benchmark includes 18 carefully curated tasks from real-world engineering and scientific domains, each standardized through resource filtering, evaluator validation, and solution collection. In addition, we provide iGym, a unified execution environment for reproducible and long-horizon evaluations. Extensive experiments show that while some agents produce novel approaches, their lack of robustness limits performance gains. These results highlight a key gap between creativity and effectiveness, underscoring the need for benchmarks that evaluate both.
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            <a href="https://www.alphaxiv.org/abs/2512.01797v1" target="_blank" rel="noopener noreferrer">
                H-神经元：关于幻觉相关神经元的存在、影响与起源
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            H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Cheng Gao, Huimin Chen, Chaojun Xiao, Zhiyi Chen, Zhiyuan Liu, Maosong Sun
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于幻觉相关神经元的研究，这属于纯粹的NLP评估基准和幻觉分析范畴，与您关注的推荐系统、搜索、广告领域的技术进展、LLM应用或Transformer架构改进无直接关联。根据您的排除标准，此类纯粹NLP中心化主题（包括幻觉、评估基准等）应被视为不相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 15:32:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01797v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01797v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span></div>
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                    Large language models (LLMs) frequently generate hallucinations -- plausible but factually incorrect outputs -- undermining their reliability. While prior work has examined hallucinations from macroscopic perspectives such as training data and objectives, the underlying neuron-level mechanisms remain largely unexplored. In this paper, we conduct a systematic investigation into hallucination-associated neurons (H-Neurons) in LLMs from three perspectives: identification, behavioral impact, and origins. Regarding their identification, we demonstrate that a remarkably sparse subset of neurons (less than $0.1\%$ of total neurons) can reliably predict hallucination occurrences, with strong generalization across diverse scenarios. In terms of behavioral impact, controlled interventions reveal that these neurons are causally linked to over-compliance behaviors. Concerning their origins, we trace these neurons back to the pre-trained base models and find that these neurons remain predictive for hallucination detection, indicating they emerge during pre-training. Our findings bridge macroscopic behavioral patterns with microscopic neural mechanisms, offering insights for developing more reliable LLMs.
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            <a href="https://www.alphaxiv.org/abs/2512.01713v1" target="_blank" rel="noopener noreferrer">
                多语言词表上的自监督借用检测
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        <div class="mb-2 text-base text-gray-700">
            Self-Supervised Borrowing Detection on Multilingual Wordlists
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tim Wientzek
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及多语言词表和借用检测，属于语言学或跨语言分析领域，与推荐系统、搜索或广告的核心技术无关。自监督学习虽然是一种通用技术，但论文聚焦于语言学特定问题，没有展示在RecSys/Search/Ads中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 14:20:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01713v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01713v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    This paper presents a fully self-supervised approach to borrowing detection in multilingual wordlists. The method combines two sources of information: PMI similarities based on a global correspondence model and a lightweight contrastive component trained on phonetic feature vectors. It further includes an automatic procedure for selecting decision thresholds without requiring labeled data. Experiments on benchmark datasets show that PMI alone already improves over existing string similarity measures such as NED and SCA, and that the combined similarity performs on par with or better than supervised baselines. An ablation study highlights the importance of character encoding, temperature settings and augmentation strategies. The approach scales to datasets of different sizes, works without manual supervision and is provided with a command-line tool that allows researchers to conduct their own studies.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01659v1" target="_blank" rel="noopener noreferrer">
                HalluGraph：基于知识图谱对齐的法律RAG系统可审计幻觉检测
            </a>
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            HalluGraph: Auditable Hallucination Detection for Legal RAG Systems via Knowledge Graph Alignment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Valentin Noël, Elimane Yassine Seidou, Charly Ken Capo-Chichi, Ghanem Amari
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及幻觉检测（Hallucination Detection），这属于'Irrelevant Topics'中明确排除的'纯粹NLP中心主题'。虽然提到了RAG系统和法律领域，但核心焦点是幻觉检测而非推荐系统、搜索或广告的直接应用。标题中没有表明任何与推荐系统、搜索或广告相关的潜在应用，也不涉及Transformer架构进展、多模态建模或LLM在推荐/搜索/广告中的直接应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 13:31:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01659v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01659v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Legal AI systems powered by retrieval-augmented generation (RAG) face a critical accountability challenge: when an AI assistant cites case law, statutes, or contractual clauses, practitioners need verifiable guarantees that generated text faithfully represents source documents. Existing hallucination detectors rely on semantic similarity metrics that tolerate entity substitutions, a dangerous failure mode when confusing parties, dates, or legal provisions can have material consequences. We introduce HalluGraph, a graph-theoretic framework that quantifies hallucinations through structural alignment between knowledge graphs extracted from context, query, and response. Our approach produces bounded, interpretable metrics decomposed into \textit{Entity Grounding} (EG), measuring whether entities in the response appear in source documents, and \textit{Relation Preservation} (RP), verifying that asserted relationships are supported by context. On structured control documents, HalluGraph achieves near-perfect discrimination ($>$400 words, $>$20 entities), HalluGraph achieves $AUC = 0.979$, while maintaining robust performance ($AUC \approx 0.89$) on challenging generative legal task, consistently outperforming semantic similarity baselines. The framework provides the transparency and traceability required for high-stakes legal applications, enabling full audit trails from generated assertions back to source passages.
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            <a href="https://www.alphaxiv.org/abs/2512.01603v1" target="_blank" rel="noopener noreferrer">
                MAC-SLU：多意图车载座舱口语理解基准
            </a>
        </h3>
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            MAC-SLU: Multi-Intent Automotive Cabin Spoken Language Understanding Benchmark
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuezhang Peng, Chonghao Cai, Ziang Liu, Shuai Fan, Sheng Jiang, Hua Xu, Yuxin Li...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于车载座舱的口语理解基准测试，属于语音处理领域，与推荐系统、搜索或广告的核心技术进展无关。虽然涉及多意图理解，但缺乏明确的连接点将其应用于RecSys/Search/Ads领域，且属于语音相关论文，根据排除标准应被排除。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 12:23:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01603v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01603v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.MM</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Spoken Language Understanding (SLU), which aims to extract user semantics to execute downstream tasks, is a crucial component of task-oriented dialog systems. Existing SLU datasets generally lack sufficient diversity and complexity, and there is an absence of a unified benchmark for the latest Large Language Models (LLMs) and Large Audio Language Models (LALMs). This work introduces MAC-SLU, a novel Multi-Intent Automotive Cabin Spoken Language Understanding Dataset, which increases the difficulty of the SLU task by incorporating authentic and complex multi-intent data. Based on MAC-SLU, we conducted a comprehensive benchmark of leading open-source LLMs and LALMs, covering methods like in-context learning, supervised fine-tuning (SFT), and end-to-end (E2E) and pipeline paradigms. Our experiments show that while LLMs and LALMs have the potential to complete SLU tasks through in-context learning, their performance still lags significantly behind SFT. Meanwhile, E2E LALMs demonstrate performance comparable to pipeline approaches and effectively avoid error propagation from speech recognition. Code\footnote{https://github.com/Gatsby-web/MAC\_SLU} and datasets\footnote{huggingface.co/datasets/Gatsby1984/MAC\_SLU} are released publicly.
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            <a href="https://www.alphaxiv.org/abs/2512.01557v1" target="_blank" rel="noopener noreferrer">
                语言多样性：评估数字空间中非洲语言的使用情况及人工智能性能
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            Language Diversity: Evaluating Language Usage and AI Performance on African Languages in Digital Spaces
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Edward Ajayi, Eudoxie Umwari, Mawuli Deku, Prosper Singadi, Jules Udahemuka, Bek...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注语言多样性评估和AI在特定语言上的性能，属于NLP评估基准范畴，与推荐系统、搜索或广告的核心技术进展无关。虽然涉及AI性能，但未涉及Transformer架构改进、LLM技术趋势或这些技术在推荐/搜索/广告领域的直接应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 11:27:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01557v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01557v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This study examines the digital representation of African languages and the challenges this presents for current language detection tools. We evaluate their performance on Yoruba, Kinyarwanda, and Amharic. While these languages are spoken by millions, their online usage on conversational platforms is often sparse, heavily influenced by English, and not representative of the authentic, monolingual conversations prevalent among native speakers. This lack of readily available authentic data online creates a challenge of scarcity of conversational data for training language models. To investigate this, data was collected from subreddits and local news sources for each language. The analysis showed a stark contrast between the two sources. Reddit data was minimal and characterized by heavy code-switching. Conversely, local news media offered a robust source of clean, monolingual language data, which also prompted more user engagement in the local language on the news publishers social media pages. Language detection models, including the specialized AfroLID and a general LLM, performed with near-perfect accuracy on the clean news data but struggled with the code-switched Reddit posts. The study concludes that professionally curated news content is a more reliable and effective source for training context-rich AI models for African languages than data from conversational platforms. It also highlights the need for future models that can process clean and code-switched text to improve the detection accuracy for African languages.
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            <a href="https://www.alphaxiv.org/abs/2512.01443v1" target="_blank" rel="noopener noreferrer">
                MEGConformer：基于Conformer的MEG解码器，用于鲁棒的语音和音素分类
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            MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xabier de Zuazo, Ibon Saratxaga, Eva Navas
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于脑磁图（MEG）信号处理用于语音/音素分类，属于生物医学信号处理领域。虽然使用了Conformer架构，但应用场景（MEG解码）与推荐系统、搜索或广告的核心技术需求无关，且没有展示这些技术如何应用于相关领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 09:25:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01443v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01443v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.NE</span><span class="category-tag">cs.SD</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present Conformer-based decoders for the LibriBrain 2025 PNPL competition, targeting two foundational MEG tasks: Speech Detection and Phoneme Classification. Our approach adapts a compact Conformer to raw 306-channel MEG signals, with a lightweight convolutional projection layer and task-specific heads. For Speech Detection, a MEG-oriented SpecAugment provided a first exploration of MEG-specific augmentation. For Phoneme Classification, we used inverse-square-root class weighting and a dynamic grouping loader to handle 100-sample averaged examples. In addition, a simple instance-level normalization proved critical to mitigate distribution shifts on the holdout split. Using the official Standard track splits and F1-macro for model selection, our best systems achieved 88.9% (Speech) and 65.8% (Phoneme) on the leaderboard, surpassing the competition baselines and ranking within the top-10 in both tasks. For further implementation details, the technical documentation, source code, and checkpoints are available at https://github.com/neural2speech/libribrain-experiments.
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            <a href="https://www.alphaxiv.org/abs/2512.01439v1" target="_blank" rel="noopener noreferrer">
                面向金融援助的多语言对话式人工智能：弥合印度金融科技领域的语言障碍
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            Multilingual Conversational AI for Financial Assistance: Bridging Language Barriers in Indian FinTech
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bharatdeep Hazarika, Arya Suneesh, Prasanna Devadiga, Pawan Kumar Rajpoot, Anshu...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于金融科技领域的特定应用（金融援助）和特定地区（印度），属于领域特定应用，与RecSys/Search/Ads的核心技术进展无关。虽然涉及对话式AI，但未提及LLM/Transformer架构创新或其在推荐/搜索/广告中的潜在应用，完全偏离了所有关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 09:23:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01439v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01439v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    India's linguistic diversity presents both opportunities and challenges for fintech platforms. While the country has 31 major languages and over 100 minor ones, only 10\% of the population understands English, creating barriers to financial inclusion. We present a multilingual conversational AI system for a financial assistance use case that supports code-mixed languages like Hinglish, enabling natural interactions for India's diverse user base. Our system employs a multi-agent architecture with language classification, function management, and multilingual response generation. Through comparative analysis of multiple language models and real-world deployment, we demonstrate significant improvements in user engagement while maintaining low latency overhead (4-8\%). This work contributes to bridging the language gap in digital financial services for emerging markets.
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            <a href="https://www.alphaxiv.org/abs/2512.01396v1" target="_blank" rel="noopener noreferrer">
                BackportBench：一个用于自动化补丁回移植的多语言基准测试
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            BackportBench: A Multilingual Benchmark for Automated Backporting of Patches
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiqing Zhong, Jiaming Huang, Pinjia He
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及软件工程中的补丁回移植基准测试，属于代码维护和软件安全领域。这与您关注的推荐系统、搜索广告、LLM技术、Transformer架构或异构数据统一建模等核心方向完全无关，没有潜在的应用关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 08:16:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01396v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01396v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SE</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CR</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Many modern software projects evolve rapidly to incorporate new features and security patches. It is important for users to update their dependencies to safer versions, but many still use older, vulnerable package versions because upgrading can be difficult and may break their existing codebase. Software developers can mitigate this problem by backporting security patches to older releases. However, manually backporting is time-consuming and error-prone. The effectiveness of existing automated backporting techniques on general software remains unclear since they typically target only code-hunk or function-level patch porting scenarios and are evaluated with imperfect metrics. To facilitate the development and evaluation of automated backporting techniques, we introduce BackportBench, the first comprehensive benchmark suite for patch backporting problem. BackportBench is a multilingual benchmark that contains 202 patch backporting problems from PyPI, Maven, and npm, each with executable Docker environments and relevant test cases. We evaluated existing patch porting methods and LLM-based techniques that have the potential to adapt to this task using BackportBench. The results show that the agentic method has outperformed traditional patch porting methods, especially on cases that require logical and structural changes. However, the performance varies across different programming languages. Based on the findings, we draw several implications for researchers and software practitioners in future work on automated backporting.
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            <a href="https://www.alphaxiv.org/abs/2512.01326v1" target="_blank" rel="noopener noreferrer">
                保护大语言模型免受提示注入攻击
            </a>
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            Securing Large Language Models (LLMs) from Prompt Injection Attacks
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Omar Farooq Khan Suri, John McCrae
        </div>
        
        
        
        
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及LLM安全（提示注入攻击防护），这属于明确的无关主题（安全、隐私）。虽然涉及LLM技术，但论文焦点是安全防护而非LLM技术本身进展或其在推荐/搜索/广告领域的应用，因此与当前关注点无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:34:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01326v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01326v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Large Language Models (LLMs) are increasingly being deployed in real-world applications, but their flexibility exposes them to prompt injection attacks. These attacks leverage the model's instruction-following ability to make it perform malicious tasks. Recent work has proposed JATMO, a task-specific fine-tuning approach that trains non-instruction-tuned base models to perform a single function, thereby reducing susceptibility to adversarial instructions. In this study, we evaluate the robustness of JATMO against HOUYI, a genetic attack framework that systematically mutates and optimizes adversarial prompts. We adapt HOUYI by introducing custom fitness scoring, modified mutation logic, and a new harness for local model testing, enabling a more accurate assessment of defense effectiveness. We fine-tuned LLaMA 2-7B, Qwen1.5-4B, and Qwen1.5-0.5B models under the JATMO methodology and compared them with a fine-tuned GPT-3.5-Turbo baseline. Results show that while JATMO reduces attack success rates relative to instruction-tuned models, it does not fully prevent injections; adversaries exploiting multilingual cues or code-related disruptors still bypass defenses. We also observe a trade-off between generation quality and injection vulnerability, suggesting that better task performance often correlates with increased susceptibility. Our results highlight both the promise and limitations of fine-tuning-based defenses and point toward the need for layered, adversarially informed mitigation strategies.
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            <a href="https://www.alphaxiv.org/abs/2512.01316v1" target="_blank" rel="noopener noreferrer">
                基于一致性约束的概率最小贝叶斯风险解码
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            Agreement-Constrained Probabilistic Minimum Bayes Risk Decoding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Koki Natsumi, Hiroyuki Deguchi, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及解码算法和贝叶斯风险最小化，属于自然语言处理中的生成模型解码技术，与推荐系统、搜索或广告的核心技术无直接关联。虽然解码技术可能间接影响文本生成质量，但论文未明确涉及异构数据处理、Transformer架构改进或LLM在推荐/搜索/广告中的直接应用，因此与当前关注点高度不相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:16:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01316v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01316v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Minimum Bayes risk (MBR) decoding generates high-quality translations by maximizing the expected utility of output candidates, but it evaluates all pairwise scores over the candidate set; hence, it takes quadratic time with respect to the number of candidates. To reduce the number of utility function calls, probabilistic MBR (PMBR) decoding partially evaluates quality scores using sampled pairs of candidates and completes the missing scores with a matrix completion algorithm. Nevertheless, it degrades the translation quality as the number of utility function calls is reduced. Therefore, to improve the trade-off between quality and cost, we propose agreement-constrained PMBR (AC-PMBR) decoding, which leverages a knowledge distilled model to guide the completion of the score matrix. Our AC-PMBR decoding improved approximation errors of matrix completion by up to 3 times and achieved higher translation quality compared with PMBR decoding at a comparable computational cost on the WMT'23 En$\leftrightarrow$De translation tasks.
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            <a href="https://www.alphaxiv.org/abs/2512.01282v1" target="_blank" rel="noopener noreferrer">
                Kardia-R1：通过基于评分标准的评判强化学习释放大型语言模型在情感支持中实现理解和共情推理的能力
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            Kardia-R1: Unleashing LLMs to Reason toward Understanding and Empathy for Emotional Support via Rubric-as-Judge Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiahao Yuan, Zhiqing Cui, Hanqing Wang, Yuansheng Gao, Yucheng Zhou, Usman Nasee...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于情感支持和共情推理，这属于心理学和心理健康应用领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及LLM和强化学习，但其应用场景（情感支持）被明确列为不相关主题，且未提及任何与RecSys/Search/Ads相关的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 04:54:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01282v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01282v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As web platforms evolve towards greater personalization and emotional complexity, conversational agents must transcend superficial empathy to demonstrate identity-aware emotional reasoning. However, existing systems face two limitations: (1) reliance on situation-centric datasets lacking persistent user identity, which hampers the capture of personalized affective nuances; and (2) dependence on opaque, coarse reward signals that hinder development of verifiable empathetic reasoning. To address these gaps, we introduce KardiaBench, a large-scale user-grounded benchmark comprising 178,080 QA pairs across 22,080 multi-turn conversations anchored to 671 real-world profiles. The dataset is constructed via a model-in-the-loop pipeline with iterative rubric-guided refinement to ensure psychological plausibility and persona consistency. This progressive empathy pipeline that integrates user comprehension, contextual reasoning, and emotion perception into conversations, followed by iterative critique and rubric-based refinement to ensure psychological plausibility, emotional fidelity, and persona consistency. Building on this, we propose Kardia-R1, a framework that trains models for interpretable, stepwise empathetic cognition. Kardia-R1 leverages Rubric-as-Judge Empathetic Reinforcement Learning (Rubric-ERL), a GRPO-based method that uses explainable, human-aligned rubric rewards to tightly couple user understanding, emotional inference, and supportive response generation. Extensive experiments across four LLM backbones demonstrate that Kardia-R1 consistently outperforms othet methods in emotion accuracy, empathy, relevance, persona consistency, and safety. Our dataset and model will be released at https://github.com/JhCircle/Kardia-R1.
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            <a href="https://www.alphaxiv.org/abs/2512.01274v1" target="_blank" rel="noopener noreferrer">
                SUPERChem：化学领域的多模态推理基准
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            SUPERChem: A Multimodal Reasoning Benchmark in Chemistry
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zehua Zhao, Zhixian Huang, Junren Li, Siyu Lin, Junting Zhou, Fengqi Cao, Kun Zh...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于化学领域的多模态基准测试，属于明确的'无关主题'范畴（医学、生物学、化学等特定领域应用）。尽管涉及多模态概念，但化学领域与推荐系统、搜索或广告的核心技术发展无直接关联，且未提及任何潜在的跨领域应用可能性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 04:46:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01274v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01274v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Current benchmarks for evaluating the chemical reasoning capabilities of Large Language Models (LLMs) are limited by oversimplified tasks, lack of process-level evaluation, and misalignment with expert-level chemistry skills. To address these issues, we introduce SUPERChem, a benchmark of 500 expert-curated reasoning-intensive chemistry problems, covering diverse subfields and provided in both multimodal and text-only formats. Original content and an iterative curation pipeline eliminate flawed items and mitigate data contamination. Each problem is paired with an expert-authored solution path, enabling Reasoning Path Fidelity (RPF) scoring to evaluate reasoning quality beyond final-answer accuracy. Evaluations against a human baseline of 40.3% accuracy show that even the best-performing model, GPT-5 (High), reaches only 38.5%, followed closely by Gemini 2.5 Pro (37.9%) and DeepSeek-V3.1-Think (37.3%). SUPERChem elicits multi-step, multimodal reasoning, reveals model-dependent effects of visual information, and distinguishes high-fidelity reasoners from heuristic ones. By providing a challenging benchmark and a reliable evaluation framework, SUPERChem aims to facilitate the advancement of LLMs toward expert-level chemical intelligence. The dataset of the benchmark is available at https://huggingface.co/datasets/ZehuaZhao/SUPERChem.
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            <a href="https://www.alphaxiv.org/abs/2512.01256v1" target="_blank" rel="noopener noreferrer">
                基于机器学习技术的Nagamese语言情感分析与情绪分类——一种低资源语言
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            Sentiment Analysis and Emotion Classification using Machine Learning Techniques for Nagamese Language - A Low-resource Language
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ekha Morang, Surhoni A. Ngullie, Sashienla Longkumer, Teisovi Angami
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于特定低资源语言（Nagamese）的情感分析和情绪分类，属于传统NLP应用领域。它不涉及推荐系统、搜索或广告的核心进展，也不包含可能应用于这些领域的LLM技术、Transformer架构创新或异构数据统一建模方法。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 04:01:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01256v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01256v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    The Nagamese language, a.k.a Naga Pidgin, is an Assamese-lexified creole language developed primarily as a means of communication in trade between the people from Nagaland and people from Assam in the north-east India. Substantial amount of work in sentiment analysis has been done for resource-rich languages like English, Hindi, etc. However, no work has been done in Nagamese language. To the best of our knowledge, this is the first attempt on sentiment analysis and emotion classification for the Nagamese Language. The aim of this work is to detect sentiments in terms of polarity (positive, negative and neutral) and basic emotions contained in textual content of Nagamese language. We build sentiment polarity lexicon of 1,195 nagamese words and use these to build features along with additional features for supervised machine learning techniques using Na"ive Bayes and Support Vector Machines. Keywords: Nagamese, NLP, sentiment analysis, machine learning
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                大型语言模型无法可靠检测JavaScript漏洞：首个系统性基准与评估
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            Large Language Models Cannot Reliably Detect Vulnerabilities in JavaScript: The First Systematic Benchmark and Evaluation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qingyuan Fei, Xin Liu, Song Li, Shujiang Wu, Jianwei Hou, Ping Chen, Zifeng Kang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM在代码漏洞检测（特别是JavaScript）方面的能力评估，属于纯粹的LLM能力测试范畴。虽然涉及LLM技术，但论文内容与推荐系统、搜索或广告领域没有直接关联，也不涉及Transformer架构改进或异构数据统一建模等关注点。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 04:00:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01255v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01255v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.SE</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Researchers have proposed numerous methods to detect vulnerabilities in JavaScript, especially those assisted by Large Language Models (LLMs). However, the actual capability of LLMs in JavaScript vulnerability detection remains questionable, necessitating systematic evaluation and comprehensive benchmarks. Unfortunately, existing benchmarks suffer from three critical limitations: (1) incomplete coverage, such as covering a limited subset of CWE types; (2) underestimation of LLM capabilities caused by unreasonable ground truth labeling; and (3) overestimation due to unrealistic cases such as using isolated vulnerable files rather than complete projects. In this paper, we introduce, for the first time, three principles for constructing a benchmark for JavaScript vulnerability detection that directly address these limitations: (1) comprehensiveness, (2) no underestimation, and (3) no overestimation. Guided by these principles, we propose FORGEJS, the first automatic benchmark generation framework for evaluating LLMs' capability in JavaScript vulnerability detection. Then, we use FORGEJS to construct ARENAJS-the first systematic benchmark for LLM-based JavaScript vulnerability detection-and further propose JUDGEJS, an automatic evaluation framework. We conduct the first systematic evaluation of LLMs for JavaScript vulnerability detection, leveraging JUDGEJS to assess seven popular commercial LLMs on ARENAJS. The results show that LLMs not only exhibit limited reasoning capabilities, but also suffer from severe robustness defects, indicating that reliable JavaScript vulnerability detection with LLMs remains an open challenge.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01198v1" target="_blank" rel="noopener noreferrer">
                传统中医翻译中意象思维的传达：基于提示工程与大型语言模型的评估框架
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Conveying Imagistic Thinking in Traditional Chinese Medicine Translation: A Prompt Engineering and LLM-Based Evaluation Framework
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiatong Han
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于传统中医翻译这一特定领域应用，属于明确的医学/生物学领域特定应用，与用户关注的推荐系统、搜索、广告等核心领域完全无关。标题中提到的提示工程和LLM评估框架仅服务于中医翻译这一特定任务，不具备在推荐/搜索/广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 02:27:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01198v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01198v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Traditional Chinese Medicine (TCM) theory is built on imagistic thinking, in which medical principles and diagnostic and therapeutic logic are structured through metaphor and metonymy. However, existing English translations largely rely on literal rendering, making it difficult for target-language readers to reconstruct the underlying conceptual networks and apply them in clinical practice. This study adopted a human-in-the-loop (HITL) framework and selected four passages from the medical canon Huangdi Neijing that are fundamental in theory. Through prompt-based cognitive scaffolding, DeepSeek V3.1 was guided to identify metaphor and metonymy in the source text and convey the theory in translation. In the evaluation stage, ChatGPT 5 Pro and Gemini 2.5 Pro were instructed by prompts to simulate three types of real-world readers. Human translations, baseline model translations, and prompt-adjusted translations were scored by the simulated readers across five cognitive dimensions, followed by structured interviews and Interpretative Phenomenological Analysis (IPA). Results show that the prompt-adjusted LLM translations perform best across all five dimensions, with high cross-model and cross-role consistency. The interview themes reveal differences between human and machine translation, effective strategies for metaphor and metonymy transfer, and readers' cognitive preferences. This study provides a cognitive, efficient, and replicable HITL methodological pathway for the translation of ancient, concept-dense texts such as TCM.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01191v1" target="_blank" rel="noopener noreferrer">
                通用大型语言模型在医学基准测试中超越临床工具
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Generalist Large Language Models Outperform Clinical Tools on Medical Benchmarks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Krithik Vishwanath, Mrigayu Ghosh, Anton Alyakin, Daniel Alexander Alber, Yindal...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学领域的基准测试和临床工具评估，这属于明确的医学/生物学领域特定应用。虽然涉及LLM性能评估，但完全在医学应用范畴内，与搜索、推荐、广告等商业应用领域无关。标题中没有任何元素表明该技术具有在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 02:14:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01191v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01191v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Specialized clinical AI assistants are rapidly entering medical practice, often framed as safer or more reliable than general-purpose large language models (LLMs). Yet, unlike frontier models, these clinical tools are rarely subjected to independent, quantitative evaluation, creating a critical evidence gap despite their growing influence on diagnosis, triage, and guideline interpretation. We assessed two widely deployed clinical AI systems (OpenEvidence and UpToDate Expert AI) against three state-of-the-art generalist LLMs (GPT-5, Gemini 3 Pro, and Claude Sonnet 4.5) using a 1,000-item mini-benchmark combining MedQA (medical knowledge) and HealthBench (clinician-alignment) tasks. Generalist models consistently outperformed clinical tools, with GPT-5 achieving the highest scores, while OpenEvidence and UpToDate demonstrated deficits in completeness, communication quality, context awareness, and systems-based safety reasoning. These findings reveal that tools marketed for clinical decision support may often lag behind frontier LLMs, underscoring the urgent need for transparent, independent evaluation before deployment in patient-facing workflows.
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            <a href="https://www.alphaxiv.org/abs/2512.02020v1" target="_blank" rel="noopener noreferrer">
                EfficientFlow：面向具身AI的高效等变流策略学习
            </a>
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            EfficientFlow: Efficient Equivariant Flow Policy Learning for Embodied AI
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jianlei Chang, Ruofeng Mei, Wei Ke, Xiangyu Xu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于具身AI（Embodied AI）中的策略学习，属于机器人控制或强化学习领域，与推荐系统、搜索或广告的核心技术无直接关联。标题中提到的'等变流'（Equivariant Flow）和'策略学习'（Policy Learning）主要应用于物理环境交互任务，无法识别出在推荐、搜索或广告系统中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:59:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02020v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02020v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Generative modeling has recently shown remarkable promise for visuomotor policy learning, enabling flexible and expressive control across diverse embodied AI tasks. However, existing generative policies often struggle with data inefficiency, requiring large-scale demonstrations, and sampling inefficiency, incurring slow action generation during inference. We introduce EfficientFlow, a unified framework for efficient embodied AI with flow-based policy learning. To enhance data efficiency, we bring equivariance into flow matching. We theoretically prove that when using an isotropic Gaussian prior and an equivariant velocity prediction network, the resulting action distribution remains equivariant, leading to improved generalization and substantially reduced data demands. To accelerate sampling, we propose a novel acceleration regularization strategy. As direct computation of acceleration is intractable for marginal flow trajectories, we derive a novel surrogate loss that enables stable and scalable training using only conditional trajectories. Across a wide range of robotic manipulation benchmarks, the proposed algorithm achieves competitive or superior performance under limited data while offering dramatically faster inference. These results highlight EfficientFlow as a powerful and efficient paradigm for high-performance embodied AI.
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            <a href="https://www.alphaxiv.org/abs/2512.02018v1" target="_blank" rel="noopener noreferrer">
                面向自动驾驶实验室的数据中心化视觉开发
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        <div class="mb-2 text-base text-gray-700">
            Data-Centric Visual Development for Self-Driving Labs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Anbang Liu, Guanzhong Hu, Jiayi Wang, Ping Guo, Han Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于自动驾驶实验室的视觉开发，属于特定领域应用（自动驾驶），与我的关注领域（推荐系统、搜索、广告）无直接关联。标题中提到的'数据中心化'和'视觉开发'均未指向推荐系统、搜索或广告中的核心问题或技术应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:59:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02018v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02018v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Self-driving laboratories offer a promising path toward reducing the labor-intensive, time-consuming, and often irreproducible workflows in the biological sciences. Yet their stringent precision requirements demand highly robust models whose training relies on large amounts of annotated data. However, this kind of data is difficult to obtain in routine practice, especially negative samples. In this work, we focus on pipetting, the most critical and precision sensitive action in SDLs. To overcome the scarcity of training data, we build a hybrid pipeline that fuses real and virtual data generation. The real track adopts a human-in-the-loop scheme that couples automated acquisition with selective human verification to maximize accuracy with minimal effort. The virtual track augments the real data using reference-conditioned, prompt-guided image generation, which is further screened and validated for reliability. Together, these two tracks yield a class-balanced dataset that enables robust bubble detection training. On a held-out real test set, a model trained entirely on automatically acquired real images reaches 99.6% accuracy, and mixing real and generated data during training sustains 99.4% accuracy while reducing collection and review load. Our approach offers a scalable and cost-effective strategy for supplying visual feedback data to SDL workflows and provides a practical solution to data scarcity in rare event detection and broader vision tasks.
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            <a href="https://www.alphaxiv.org/abs/2512.02017v1" target="_blank" rel="noopener noreferrer">
                视觉同步：通过跨视角物体运动实现多摄像头同步
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            Visual Sync: Multi-Camera Synchronization via Cross-View Object Motion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shaowei Liu, David Yifan Yao, Saurabh Gupta, Shenlong Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的多摄像头同步技术，属于纯粹的视觉处理范畴。虽然标题提到“跨视角物体运动”，但这本质上是视觉传感器融合问题，与推荐系统、搜索或广告的核心技术（如排序、用户建模、内容理解等）没有直接关联。该技术没有明显的应用场景可以转化为推荐系统、搜索或广告领域的解决方案。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:59:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02017v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02017v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.RO</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Today, people can easily record memorable moments, ranging from concerts, sports events, lectures, family gatherings, and birthday parties with multiple consumer cameras. However, synchronizing these cross-camera streams remains challenging. Existing methods assume controlled settings, specific targets, manual correction, or costly hardware. We present VisualSync, an optimization framework based on multi-view dynamics that aligns unposed, unsynchronized videos at millisecond accuracy. Our key insight is that any moving 3D point, when co-visible in two cameras, obeys epipolar constraints once properly synchronized. To exploit this, VisualSync leverages off-the-shelf 3D reconstruction, feature matching, and dense tracking to extract tracklets, relative poses, and cross-view correspondences. It then jointly minimizes the epipolar error to estimate each camera's time offset. Experiments on four diverse, challenging datasets show that VisualSync outperforms baseline methods, achieving an median synchronization error below 50 ms.
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            <a href="https://www.alphaxiv.org/abs/2512.02016v1" target="_blank" rel="noopener noreferrer">
                生成视频中的物体比看起来更慢：模型遭受亚地球重力影响且不了解伽利略原理...目前如此
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Objects in Generated Videos Are Slower Than They Appear: Models Suffer Sub-Earth Gravity and Don't Know Galileo's Principle...for now
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Varun Varma Thozhiyoor, Shivam Tripathi, Venkatesh Babu Radhakrishnan, Anand Bha...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向视频生成中的物理规律建模问题，属于纯粹的视觉内容生成领域。虽然涉及生成模型，但专注于视频物理属性而非推荐/搜索/广告应用，且未提及任何与异质数据建模、Transformer架构或LLM技术相关的概念，与所有关注领域均无直接关联。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:59:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02016v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02016v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Video generators are increasingly evaluated as potential world models, which requires them to encode and understand physical laws. We investigate their representation of a fundamental law: gravity. Out-of-the-box video generators consistently generate objects falling at an effectively slower acceleration. However, these physical tests are often confounded by ambiguous metric scale. We first investigate if observed physical errors are artifacts of these ambiguities (e.g., incorrect frame rate assumptions). We find that even temporal rescaling cannot correct the high-variance gravity artifacts. To rigorously isolate the underlying physical representation from these confounds, we introduce a unit-free, two-object protocol that tests the timing ratio $t_1^2/t_2^2 = h_1/h_2$, a relationship independent of $g$, focal length, and scale. This relative test reveals violations of Galileo's equivalence principle. We then demonstrate that this physical gap can be partially mitigated with targeted specialization. A lightweight low-rank adaptor fine-tuned on only 100 single-ball clips raises $g_{\mathrm{eff}}$ from $1.81\,\mathrm{m/s^2}$ to $6.43\,\mathrm{m/s^2}$ (reaching $65\%$ of terrestrial gravity). This specialist adaptor also generalizes zero-shot to two-ball drops and inclined planes, offering initial evidence that specific physical laws can be corrected with minimal data.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.02015v1" target="_blank" rel="noopener noreferrer">
                基于3D点轨迹的生成式视频运动编辑
            </a>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Generative Video Motion Editing with 3D Point Tracks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yao-Chih Lee, Zhoutong Zhang, Jiahui Huang, Jui-Hsien Wang, Joon-Young Lee, Jia-...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向计算机视觉领域的视频运动编辑技术，涉及3D点轨迹和生成方法。这属于纯粹的视觉处理范畴，与推荐系统、搜索或广告的核心技术没有直接关联。标题中没有任何元素表明该技术可应用于异构数据处理、序列建模或推荐/搜索/广告领域的其他相关任务。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:59:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02015v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02015v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Camera and object motions are central to a video's narrative. However, precisely editing these captured motions remains a significant challenge, especially under complex object movements. Current motion-controlled image-to-video (I2V) approaches often lack full-scene context for consistent video editing, while video-to-video (V2V) methods provide viewpoint changes or basic object translation, but offer limited control over fine-grained object motion. We present a track-conditioned V2V framework that enables joint editing of camera and object motion. We achieve this by conditioning a video generation model on a source video and paired 3D point tracks representing source and target motions. These 3D tracks establish sparse correspondences that transfer rich context from the source video to new motions while preserving spatiotemporal coherence. Crucially, compared to 2D tracks, 3D tracks provide explicit depth cues, allowing the model to resolve depth order and handle occlusions for precise motion editing. Trained in two stages on synthetic and real data, our model supports diverse motion edits, including joint camera/object manipulation, motion transfer, and non-rigid deformation, unlocking new creative potential in video editing.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.02009v1" target="_blank" rel="noopener noreferrer">
                AirSim360：无人机视角下的全景仿真平台
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            AirSim360: A Panoramic Simulation Platform within Drone View
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xian Ge, Yuling Pan, Yuhang Zhang, Xiang Li, Weijun Zhang, Dizhe Zhang, Zhaolian...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向无人机仿真平台，属于纯粹的视觉/3D视觉领域，与推荐系统、搜索或广告的核心技术、LLM应用或Transformer架构进步无直接关联。根据用户指定的无关主题，此类纯视觉论文若无明确相关性应排除。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:59:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02009v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02009v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The field of 360-degree omnidirectional understanding has been receiving increasing attention for advancing spatial intelligence. However, the lack of large-scale and diverse data remains a major limitation. In this work, we propose AirSim360, a simulation platform for omnidirectional data from aerial viewpoints, enabling wide-ranging scene sampling with drones. Specifically, AirSim360 focuses on three key aspects: a render-aligned data and labeling paradigm for pixel-level geometric, semantic, and entity-level understanding; an interactive pedestrian-aware system for modeling human behavior; and an automated trajectory generation paradigm to support navigation tasks. Furthermore, we collect more than 60K panoramic samples and conduct extensive experiments across various tasks to demonstrate the effectiveness of our simulator. Unlike existing simulators, our work is the first to systematically model the 4D real world under an omnidirectional setting. The entire platform, including the toolkit, plugins, and collected datasets, will be made publicly available at https://insta360-research-team.github.io/AirSim360-website.
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            <a href="https://www.alphaxiv.org/abs/2512.02006v1" target="_blank" rel="noopener noreferrer">
                MV-TAP：多视角视频中的任意点追踪
            </a>
        </h3>
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        <div class="mb-2 text-base text-gray-700">
            MV-TAP: Tracking Any Point in Multi-View Videos
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jahyeok Koo, Inès Hyeonsu Kim, Mungyeom Kim, Junghyun Park, Seohyun Park, Jaeyeo...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于计算机视觉中的多视角视频点追踪技术，属于纯粹的视觉处理领域。虽然标题中的“追踪”概念在推荐系统或搜索中可能涉及用户行为序列建模，但该论文明确针对视频中的物理点追踪，与推荐系统、搜索或广告中的用户行为建模、内容理解或排序任务没有直接关联。没有证据表明该技术可应用于异构数据处理、Transformer架构改进或LLM技术，因此与当前关注点高度不相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:59:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02006v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02006v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multi-view camera systems enable rich observations of complex real-world scenes, and understanding dynamic objects in multi-view settings has become central to various applications. In this work, we present MV-TAP, a novel point tracker that tracks points across multi-view videos of dynamic scenes by leveraging cross-view information. MV-TAP utilizes camera geometry and a cross-view attention mechanism to aggregate spatio-temporal information across views, enabling more complete and reliable trajectory estimation in multi-view videos. To support this task, we construct a large-scale synthetic training dataset and real-world evaluation sets tailored for multi-view tracking. Extensive experiments demonstrate that MV-TAP outperforms existing point-tracking methods on challenging benchmarks, establishing an effective baseline for advancing research in multi-view point tracking.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.02005v1" target="_blank" rel="noopener noreferrer">
                从音频中学习视觉可供性
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        <div class="mb-2 text-base text-gray-700">
            Learning Visual Affordance from Audio
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lidong Lu, Guo Chen, Zhu Wei, Yicheng Liu, Tong Lu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及视觉可供性（视觉感知与交互）和音频模态的跨模态学习，属于纯粹的视觉-音频跨模态研究领域。虽然标题提到“学习”，但这与推荐系统、搜索或广告的核心技术、LLM赋能技术、Transformer架构进展、LLM直接应用或异构数据统一建模均无直接关联。该研究更偏向计算机视觉和音频处理的交叉领域，没有明确指向推荐/搜索/广告场景的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:58:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.02005v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.02005v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce Audio-Visual Affordance Grounding (AV-AG), a new task that segments object interaction regions from action sounds. Unlike existing approaches that rely on textual instructions or demonstration videos, which often limited by ambiguity or occlusion, audio provides real-time, semantically rich, and visually independent cues for affordance grounding, enabling more intuitive understanding of interaction regions. To support this task, we construct the first AV-AG dataset, comprising a large collection of action sounds, object images, and pixel-level affordance annotations. The dataset also includes an unseen subset to evaluate zero-shot generalization. Furthermore, we propose AVAGFormer, a model equipped with a semantic-conditioned cross-modal mixer and a dual-head decoder that effectively fuses audio and visual signals for mask prediction. Experiments show that AVAGFormer achieves state-of-the-art performance on AV-AG, surpassing baselines from related tasks. Comprehensive analyses highlight the distinctions between AV-AG and AVS, the benefits of end-to-end modeling, and the contribution of each component. Code and dataset have been released on https://jscslld.github.io/AVAGFormer/.
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            <a href="https://www.alphaxiv.org/abs/2512.01993v1" target="_blank" rel="noopener noreferrer">
                RoaD：作为自动驾驶策略闭环监督微调演示的轨迹展开
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            RoaD: Rollouts as Demonstrations for Closed-Loop Supervised Fine-Tuning of Autonomous Driving Policies
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guillermo Garcia-Cobo, Maximilian Igl, Peter Karkus, Zhejun Zhang, Michael Watso...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于自动驾驶领域，属于明确的无关主题（医学、生物学、化学、物理学或其他领域特定应用）。标题中提到的“闭环监督微调”和“自动驾驶策略”与推荐系统、搜索或广告的核心进展、使能技术或直接应用没有关联。没有证据表明该技术有潜力应用于推荐系统、搜索或广告领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:52:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01993v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01993v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Autonomous driving policies are typically trained via open-loop behavior cloning of human demonstrations. However, such policies suffer from covariate shift when deployed in closed loop, leading to compounding errors. We introduce Rollouts as Demonstrations (RoaD), a simple and efficient method to mitigate covariate shift by leveraging the policy's own closed-loop rollouts as additional training data. During rollout generation, RoaD incorporates expert guidance to bias trajectories toward high-quality behavior, producing informative yet realistic demonstrations for fine-tuning. This approach enables robust closed-loop adaptation with orders of magnitude less data than reinforcement learning, and avoids restrictive assumptions of prior closed-loop supervised fine-tuning (CL-SFT) methods, allowing broader applications domains including end-to-end driving. We demonstrate the effectiveness of RoaD on WOSAC, a large-scale traffic simulation benchmark, where it performs similar or better than the prior CL-SFT method; and in AlpaSim, a high-fidelity neural reconstruction-based simulator for end-to-end driving, where it improves driving score by 41\% and reduces collisions by 54\%.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01989v1" target="_blank" rel="noopener noreferrer">
                PAI-Bench：面向物理人工智能的综合性基准测试
            </a>
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        <div class="mb-2 text-base text-gray-700">
            PAI-Bench: A Comprehensive Benchmark For Physical AI
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fengzhe Zhou, Jiannan Huang, Jialuo Li, Deva Ramanan, Humphrey Shi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向物理人工智能（Physical AI）的基准测试，属于机器人学、具身智能或物理系统控制的特定领域。虽然基准测试本身是技术性的，但物理AI与推荐系统、搜索或广告的核心技术栈（大规模语言模型、Transformer架构、异构数据处理）没有直接关联。该主题更接近机器人技术或特定领域应用，而非当前关注的LLM技术、推荐系统架构或搜索广告算法。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:47:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01989v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01989v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Physical AI aims to develop models that can perceive and predict real-world dynamics; yet, the extent to which current multi-modal large language models and video generative models support these abilities is insufficiently understood. We introduce Physical AI Bench (PAI-Bench), a unified and comprehensive benchmark that evaluates perception and prediction capabilities across video generation, conditional video generation, and video understanding, comprising 2,808 real-world cases with task-aligned metrics designed to capture physical plausibility and domain-specific reasoning. Our study provides a systematic assessment of recent models and shows that video generative models, despite strong visual fidelity, often struggle to maintain physically coherent dynamics, while multi-modal large language models exhibit limited performance in forecasting and causal interpretation. These observations suggest that current systems are still at an early stage in handling the perceptual and predictive demands of Physical AI. In summary, PAI-Bench establishes a realistic foundation for evaluating Physical AI and highlights key gaps that future systems must address.
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            <a href="https://www.alphaxiv.org/abs/2512.01988v1" target="_blank" rel="noopener noreferrer">
                Artemis：用于感知策略学习的结构化视觉推理
            </a>
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            Artemis: Structured Visual Reasoning for Perception Policy Learning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wei Tang, Yanpeng Sun, Shan Zhang, Xiaofan Li, Piotr Koniusz, Wei Li, Na Zhao, Z...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于视觉感知和策略学习，属于纯粹的视觉与强化学习交叉领域。虽然提到了“结构化推理”，但上下文是视觉感知策略，没有表明与推荐系统、搜索或广告有任何潜在应用关联。根据用户明确的排除标准，这属于“Purely Vision... papers without clear relevance to RecSys/Search/Ads”和“Reinforcement Learning (RL) papers without clear relevance to RecSys/Search/Ads”的范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:45:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01988v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01988v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent reinforcement-learning frameworks for visual perception policy have begun to incorporate intermediate reasoning chains expressed in natural language. Empirical observations indicate that such purely linguistic intermediate reasoning often reduces performance on perception tasks. We argue that the core issue lies not in reasoning per se but in the form of reasoning: while these chains perform semantic reasoning in an unstructured linguistic space, visual perception requires reasoning in a spatial and object-centric space. In response, we introduce Artemis, a perception-policy learning framework that performs structured proposal-based reasoning, where each intermediate step is represented as a (label, bounding-box) pair capturing a verifiable visual state. This design enables explicit tracking of intermediate states, direct supervision for proposal quality, and avoids ambiguity introduced by language-based reasoning. Artemis is built on Qwen2.5-VL-3B, achieves strong performance on grounding and detection task and exhibits substantial generalization to counting and geometric-perception tasks. The consistent improvements across these diverse settings confirm that aligning reasoning with spatial representations enhances perception-policy learning. Owing to its strengthened visual reasoning, Artemis also achieves competitive performance on general MLLM benchmarks, illustrating that spatially grounded reasoning provides a principled route toward scalable and general perception policies.
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            <a href="https://www.alphaxiv.org/abs/2512.01975v1" target="_blank" rel="noopener noreferrer">
                SGDiff：基于场景图引导扩散模型的图像协同分割与字幕生成
            </a>
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            SGDiff: Scene Graph Guided Diffusion Model for Image Collaborative SegCaptioning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xu Zhang, Jin Yuan, Hanwang Zhang, Guojin Zhong, Yongsheng Zang, Jiacheng Lin, Z...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于计算机视觉领域的图像分割与字幕生成任务，属于纯粹的视觉应用范畴。虽然涉及扩散模型技术，但未提及任何与推荐系统、搜索或广告相关的潜在应用场景，完全属于被排除的'纯粹视觉'类别。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:33:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01975v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01975v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Controllable image semantic understanding tasks, such as captioning or segmentation, necessitate users to input a prompt (e.g., text or bounding boxes) to predict a unique outcome, presenting challenges such as high-cost prompt input or limited information output. This paper introduces a new task ``Image Collaborative Segmentation and Captioning'' (SegCaptioning), which aims to translate a straightforward prompt, like a bounding box around an object, into diverse semantic interpretations represented by (caption, masks) pairs, allowing flexible result selection by users. This task poses significant challenges, including accurately capturing a user's intention from a minimal prompt while simultaneously predicting multiple semantically aligned caption words and masks. Technically, we propose a novel Scene Graph Guided Diffusion Model that leverages structured scene graph features for correlated mask-caption prediction. Initially, we introduce a Prompt-Centric Scene Graph Adaptor to map a user's prompt to a scene graph, effectively capturing his intention. Subsequently, we employ a diffusion process incorporating a Scene Graph Guided Bimodal Transformer to predict correlated caption-mask pairs by uncovering intricate correlations between them. To ensure accurate alignment, we design a Multi-Entities Contrastive Learning loss to explicitly align visual and textual entities by considering inter-modal similarity, resulting in well-aligned caption-mask pairs. Extensive experiments conducted on two datasets demonstrate that SGDiff achieves superior performance in SegCaptioning, yielding promising results for both captioning and segmentation tasks with minimal prompt input.
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            <a href="https://www.alphaxiv.org/abs/2512.01960v1" target="_blank" rel="noopener noreferrer">
                SpriteHand：基于自回归视频生成的实时多功能手部-物体交互
            </a>
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            SpriteHand: Real-Time Versatile Hand-Object Interaction with Autoregressive Video Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zisu Li, Hengye Lyu, Jiaxin Shi, Yufeng Zeng, Mingming Fan, Hanwang Zhang, Chen ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于手部-物体交互的实时视频生成，属于计算机视觉和图形学领域。虽然涉及生成模型，但其核心是手部姿态估计和3D视觉，与推荐系统、搜索或广告的排名、检索、用户建模等核心问题没有直接关联。该技术缺乏在RecSys/Search/Ads领域的明确应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 18:13:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01960v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01960v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.HC</span></div>
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                    Modeling and synthesizing complex hand-object interactions remains a significant challenge, even for state-of-the-art physics engines. Conventional simulation-based approaches rely on explicitly defined rigid object models and pre-scripted hand gestures, making them inadequate for capturing dynamic interactions with non-rigid or articulated entities such as deformable fabrics, elastic materials, hinge-based structures, furry surfaces, or even living creatures. In this paper, we present SpriteHand, an autoregressive video generation framework for real-time synthesis of versatile hand-object interaction videos across a wide range of object types and motion patterns. SpriteHand takes as input a static object image and a video stream in which the hands are imagined to interact with the virtual object embedded in a real-world scene, and generates corresponding hand-object interaction effects in real time. Our model employs a causal inference architecture for autoregressive generation and leverages a hybrid post-training approach to enhance visual realism and temporal coherence. Our 1.3B model supports real-time streaming generation at around 18 FPS and 640x368 resolution, with an approximate 150 ms latency on a single NVIDIA RTX 5090 GPU, and more than a minute of continuous output. Experiments demonstrate superior visual quality, physical plausibility, and interaction fidelity compared to both generative and engine-based baselines.
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            <a href="https://www.alphaxiv.org/abs/2512.01946v1" target="_blank" rel="noopener noreferrer">
                Guardian：利用视觉语言模型检测机器人规划与执行错误
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            Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Paul Pacaud, Ricardo Garcia, Shizhe Chen, Cordelia Schmid
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于机器人领域的错误检测应用，属于纯粹的机器人学/计算机视觉方向。虽然涉及视觉语言模型技术，但应用场景（机器人规划与执行）与搜索/推荐/广告领域无直接关联，也不符合将异构数据作为多模态处理的类比场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:57:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01946v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01946v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                    Robust robotic manipulation requires reliable failure detection and recovery. Although current Vision-Language Models (VLMs) show promise, their accuracy and generalization are limited by the scarcity of failure data. To address this data gap, we propose an automatic robot failure synthesis approach that procedurally perturbs successful trajectories to generate diverse planning and execution failures. This method produces not only binary classification labels but also fine-grained failure categories and step-by-step reasoning traces in both simulation and the real world. With it, we construct three new failure detection benchmarks: RLBench-Fail, BridgeDataV2-Fail, and UR5-Fail, substantially expanding the diversity and scale of existing failure datasets. We then train Guardian, a VLM with multi-view images for detailed failure reasoning and detection. Guardian achieves state-of-the-art performance on both existing and newly introduced benchmarks. It also effectively improves task success rates when integrated into a state-of-the-art manipulation system in simulation and real robots, demonstrating the impact of our generated failure data.
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            <a href="https://www.alphaxiv.org/abs/2512.01934v1" target="_blank" rel="noopener noreferrer">
                基于对抗轨迹的物理身份转移攻击针对多目标跟踪
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        <div class="mb-2 text-base text-gray-700">
            Physical ID-Transfer Attacks against Multi-Object Tracking via Adversarial Trajectory
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenyi Wang, Yanmao Man, Raymond Muller, Ming Li, Z. Berkay Celik, Ryan Gerdes, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉中的多目标跟踪和对抗性攻击，属于纯粹视觉领域的研究。虽然提到了物理攻击，但这与推荐系统、搜索或广告的核心技术无关，也不涉及LLM、Transformer架构或异构数据建模。该研究没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:47:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01934v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01934v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multi-Object Tracking (MOT) is a critical task in computer vision, with applications ranging from surveillance systems to autonomous driving. However, threats to MOT algorithms have yet been widely studied. In particular, incorrect association between the tracked objects and their assigned IDs can lead to severe consequences, such as wrong trajectory predictions. Previous attacks against MOT either focused on hijacking the trackers of individual objects, or manipulating the tracker IDs in MOT by attacking the integrated object detection (OD) module in the digital domain, which are model-specific, non-robust, and only able to affect specific samples in offline datasets. In this paper, we present AdvTraj, the first online and physical ID-manipulation attack against tracking-by-detection MOT, in which an attacker uses adversarial trajectories to transfer its ID to a targeted object to confuse the tracking system, without attacking OD. Our simulation results in CARLA show that AdvTraj can fool ID assignments with 100% success rate in various scenarios for white-box attacks against SORT, which also have high attack transferability (up to 93% attack success rate) against state-of-the-art (SOTA) MOT algorithms due to their common design principles. We characterize the patterns of trajectories generated by AdvTraj and propose two universal adversarial maneuvers that can be performed by a human walker/driver in daily scenarios. Our work reveals under-explored weaknesses in the object association phase of SOTA MOT systems, and provides insights into enhancing the robustness of such systems.
                </div>
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            <a href="https://www.alphaxiv.org/abs/2512.01922v1" target="_blank" rel="noopener noreferrer">
                Med-VCD：通过视觉对比解码缓解医学大型视觉语言模型的幻觉问题
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            Med-VCD: Mitigating Hallucination for Medical Large Vision Language Models through Visual Contrastive Decoding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zahra Mahdavi, Zahra Khodakaramimaghsoud, Hooman Khaloo, Sina Bakhshandeh Talesh...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的视觉语言模型幻觉缓解，属于医疗应用领域，与推荐系统、搜索或广告无关。虽然涉及视觉语言模型技术，但明确针对医学场景，属于被排除的'Medical, Biology, Chemistry, Physics or other domain-specific applications'类别。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:40:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01922v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01922v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large vision-language models (LVLMs) are now central to healthcare applications such as medical visual question answering and imaging report generation. Yet, these models remain vulnerable to hallucination outputs that appear plausible but are in fact incorrect. In the natural image domain, several decoding strategies have been proposed to mitigate hallucinations by reinforcing visual evidence, but most rely on secondary decoding or rollback procedures that substantially slow inference. Moreover, existing solutions are often domain-specific and may introduce misalignment between modalities or between generated and ground-truth content. We introduce Med-VCD, a sparse visual-contrastive decoding method that mitigates hallucinations in medical LVLMs without the time overhead of secondary decoding. Med-VCD incorporates a novel token-sparsification strategy that selects visually informed tokens on the fly, trimming redundancy while retaining critical visual context and thus balancing efficiency with reliability. Evaluations on eight medical datasets, spanning ophthalmology, radiology, and pathology tasks in visual question answering, report generation, and dedicated hallucination benchmarks, show that Med-VCD raises factual accuracy by an average of 13\% and improves hallucination accuracy by 6\% relative to baseline medical LVLMs.
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            <a href="https://www.alphaxiv.org/abs/2512.01913v1" target="_blank" rel="noopener noreferrer">
                医学图像配准进展的解耦：超越趋势驱动架构，迈向领域特定策略
            </a>
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            Disentangling Progress in Medical Image Registration: Beyond Trend-Driven Architectures towards Domain-Specific Strategies
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bailiang Jian, Jiazhen Pan, Rohit Jena, Morteza Ghahremani, Hongwei Bran Li, Dan...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学图像配准这一医学领域特定应用，属于"Irrelevant Topics"中明确排除的"Medical, Biology, Chemistry, Physics or other domain-specific applications"范畴。虽然涉及架构讨论，但核心是医学图像处理，与推荐系统、搜索或广告的技术焦点无直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:30:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01913v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01913v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Medical image registration drives quantitative analysis across organs, modalities, and patient populations. Recent deep learning methods often combine low-level "trend-driven" computational blocks from computer vision, such as large-kernel CNNs, Transformers, and state-space models, with high-level registration-specific designs like motion pyramids, correlation layers, and iterative refinement. Yet, their relative contributions remain unclear and entangled. This raises a central question: should future advances in registration focus on importing generic architectural trends or on refining domain-specific design principles? Through a modular framework spanning brain, lung, cardiac, and abdominal registration, we systematically disentangle the influence of these two paradigms. Our evaluation reveals that low-level "trend-driven" computational blocks offer only marginal or inconsistent gains, while high-level registration-specific designs consistently deliver more accurate, smoother, and more robust deformations. These domain priors significantly elevate the performance of a standard U-Net baseline, far more than variants incorporating "trend-driven" blocks, achieving an average relative improvement of $\sim3\%$. All models and experiments are released within a transparent, modular benchmark that enables plug-and-play comparison for new architectures and registration tasks (https://github.com/BailiangJ/rethink-reg). This dynamic and extensible platform establishes a common ground for reproducible and fair evaluation, inviting the community to isolate genuine methodological contributions from domain priors. Our findings advocate a shift in research emphasis: from following architectural trends to embracing domain-specific design principles as the true drivers of progress in learning-based medical image registration.
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            <a href="https://www.alphaxiv.org/abs/2512.01895v1" target="_blank" rel="noopener noreferrer">
                StyleYourSmile：无需配对多风格数据的跨域人脸表情重定向
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            StyleYourSmile: Cross-Domain Face Retargeting Without Paired Multi-Style Data
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Avirup Dey, Vinay Namboodiri
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的人脸表情重定向，属于纯粹的视觉处理任务，与推荐系统、搜索或广告的核心技术没有直接关联。虽然涉及跨域技术，但未展示在异构数据处理或统一建模方面的潜力，无法应用于推荐、搜索或广告领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:14:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01895v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01895v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Cross-domain face retargeting requires disentangled control over identity, expressions, and domain-specific stylistic attributes. Existing methods, typically trained on real-world faces, either fail to generalize across domains, need test-time optimizations, or require fine-tuning with carefully curated multi-style datasets to achieve domain-invariant identity representations. In this work, we introduce \textit{StyleYourSmile}, a novel one-shot cross-domain face retargeting method that eliminates the need for curated multi-style paired data. We propose an efficient data augmentation strategy alongside a dual-encoder framework, for extracting domain-invariant identity cues and capturing domain-specific stylistic variations. Leveraging these disentangled control signals, we condition a diffusion model to retarget facial expressions across domains. Extensive experiments demonstrate that \textit{StyleYourSmile} achieves superior identity preservation and retargeting fidelity across a wide range of visual domains.
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            <a href="https://www.alphaxiv.org/abs/2512.01889v1" target="_blank" rel="noopener noreferrer">
                KM-ViPE：面向开放词汇语义SLAM的在线紧耦合视觉-语言-几何融合
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            KM-ViPE: Online Tightly Coupled Vision-Language-Geometry Fusion for Open-Vocabulary Semantic SLAM
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zaid Nasser, Mikhail Iumanov, Tianhao Li, Maxim Popov, Jaafar Mahmoud, Malik Moh...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于机器人领域的视觉-语言-几何融合和语义SLAM（同时定位与地图构建），属于纯粹的计算机视觉和机器人学应用。虽然涉及视觉-语言模型，但其应用场景（机器人导航、环境理解）与推荐系统、搜索或广告领域没有直接关联，也不符合将异构数据作为不同模态进行统一建模的类比要求。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:10:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01889v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01889v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present KM-ViPE (Knowledge Mapping Video Pose Engine), a real-time open-vocabulary SLAM framework for uncalibrated monocular cameras in dynamic environments. Unlike systems requiring depth sensors and offline calibration, KM-ViPE operates directly on raw RGB streams, making it ideal for ego-centric applications and harvesting internet-scale video data for training. KM-ViPE tightly couples DINO visual features with geometric constraints through a high-level features based adaptive robust kernel that handles both moving objects and movable static objects (e.g., moving furniture in ego-centric views). The system performs simultaneous online localization and open-vocabulary semantic mapping by fusing geometric and deep visual features aligned with language embeddings. Our results are competitive with state-of-the-art approaches, while existing solutions either operate offline, need depth data and/or odometry estimation, or lack dynamic scene robustness. KM-ViPE benefits from internet-scale training and uniquely combines online operation, uncalibrated monocular input, and robust handling of dynamic scenes, which makes it a good fit for autonomous robotics and AR/VR applications and advances practical spatial intelligence capabilities for embodied AI.
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            <a href="https://www.alphaxiv.org/abs/2512.01885v1" target="_blank" rel="noopener noreferrer">
                TransientTrack：基于瞬态荧光信号的癌细胞高级多目标追踪与分类
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            TransientTrack: Advanced Multi-Object Tracking and Classification of Cancer Cells with Transient Fluorescent Signals
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Florian Bürger, Martim Dias Gomes, Nica Gutu, Adrián E. Granada, Noémie Moreau, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学/生物学领域的癌细胞追踪与分类应用，属于明确的“Irrelevant Topics”中列出的“Medical, Biology, Chemistry, Physics or other domain-specific applications”。论文内容与推荐系统、搜索、广告、LLM技术或Transformer架构等当前关注领域无直接关联，也无潜在应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 17:08:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01885v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01885v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">q-bio.CB</span><span class="category-tag">q-bio.QM</span></div>
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                    Tracking cells in time-lapse videos is an essential technique for monitoring cell population dynamics at a single-cell level. Current methods for cell tracking are developed on videos with mostly single, constant signals and do not detect pivotal events such as cell death. Here, we present TransientTrack, a deep learning-based framework for cell tracking in multi-channel microscopy video data with transient fluorescent signals that fluctuate over time following processes such as the circadian rhythm of cells. By identifying key cellular events - mitosis (cell division) and apoptosis (cell death) our method allows us to build complete trajectories, including cell lineage information. TransientTrack is lightweight and performs matching on cell detection embeddings directly, without the need for quantification of tracking-specific cell features. Furthermore, our approach integrates Transformer Networks, multi-stage matching using all detection boxes, and the interpolation of missing tracklets with the Kalman Filter. This unified framework achieves strong performance across diverse conditions, effectively tracking cells and capturing cell division and death. We demonstrate the use of TransientTrack in an analysis of the efficacy of a chemotherapeutic drug at a single-cell level. The proposed framework could further advance quantitative studies of cancer cell dynamics, enabling detailed characterization of treatment response and resistance mechanisms. The code is available at https://github.com/bozeklab/TransientTrack.
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            <a href="https://www.alphaxiv.org/abs/2512.01850v1" target="_blank" rel="noopener noreferrer">
                注册任意点：通过流匹配扩展3D点云配准
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            Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yue Pan, Tao Sun, Liyuan Zhu, Lucas Nunes, Iro Armeni, Jens Behley, Cyrill Stach...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于3D点云配准的计算机视觉技术，属于纯粹的3D视觉领域。虽然流匹配可能涉及序列建模，但该工作没有展示与推荐系统、搜索或广告领域的明确相关性，也不涉及Transformer架构、LLM技术或异构数据统一建模。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 16:36:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01850v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01850v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Point cloud registration aligns multiple unposed point clouds into a common frame, and is a core step for 3D reconstruction and robot localization. In this work, we cast registration as conditional generation: a learned continuous, point-wise velocity field transports noisy points to a registered scene, from which the pose of each view is recovered. Unlike previous methods that conduct correspondence matching to estimate the transformation between a pair of point clouds and then optimize the pairwise transformations to realize multi-view registration, our model directly generates the registered point cloud. With a lightweight local feature extractor and test-time rigidity enforcement, our approach achieves state-of-the-art results on pairwise and multi-view registration benchmarks, particularly with low overlap, and generalizes across scales and sensor modalities. It further supports downstream tasks including relocalization, multi-robot SLAM, and multi-session map merging. Source code available at: https://github.com/PRBonn/RAP.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01843v1" target="_blank" rel="noopener noreferrer">
                PhyDetEx：检测并解释文本到视频模型的物理合理性
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            PhyDetEx: Detecting and Explaining the Physical Plausibility of T2V Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zeqing Wang, Keze Wang, Lei Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于文本到视频（T2V）模型的物理合理性检测与解释，属于纯粹的视觉/视频生成领域。虽然涉及模型评估，但未提及任何与推荐系统、搜索或广告相关的应用场景，也不属于核心LLM技术、Transformer架构进展或异构数据统一建模的研究范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 16:28:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01843v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01843v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Driven by the growing capacity and training scale, Text-to-Video (T2V) generation models have recently achieved substantial progress in video quality, length, and instruction-following capability. However, whether these models can understand physics and generate physically plausible videos remains a question. While Vision-Language Models (VLMs) have been widely used as general-purpose evaluators in various applications, they struggle to identify the physically impossible content from generated videos. To investigate this issue, we construct a \textbf{PID} (\textbf{P}hysical \textbf{I}mplausibility \textbf{D}etection) dataset, which consists of a \textit{test split} of 500 manually annotated videos and a \textit{train split} of 2,588 paired videos, where each implausible video is generated by carefully rewriting the caption of its corresponding real-world video to induce T2V models producing physically implausible content. With the constructed dataset, we introduce a lightweight fine-tuning approach, enabling VLMs to not only detect physically implausible events but also generate textual explanations on the violated physical principles. Taking the fine-tuned VLM as a physical plausibility detector and explainer, namely \textbf{PhyDetEx}, we benchmark a series of state-of-the-art T2V models to assess their adherence to physical laws. Our findings show that although recent T2V models have made notable progress toward generating physically plausible content, understanding and adhering to physical laws remains a challenging issue, especially for open-source models. Our dataset, training code, and checkpoints are available at \href{https://github.com/Zeqing-Wang/PhyDetEx}{https://github.com/Zeqing-Wang/PhyDetEx}.
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            <a href="https://www.alphaxiv.org/abs/2512.01830v1" target="_blank" rel="noopener noreferrer">
                OpenREAD：基于LLM作为评判器的强化开放式推理用于端到端自动驾驶
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            OpenREAD: Reinforced Open-Ended Reasoing for End-to-End Autonomous Driving with LLM-as-Critic
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Songyan Zhang, Wenhui Huang, Zhan Chen, Chua Jiahao Collister, Qihang Huang, Che...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于自动驾驶领域，属于您指定的不相关主题（非RecSys/Search/Ads领域）。标题中提到的LLM应用（LLM-as-Critic）和强化学习（Reinforced）均服务于自动驾驶这一核心目标，与推荐系统、搜索或广告的技术进步或应用没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 16:11:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01830v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01830v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recently, two-stage fine-tuning strategies, e.g., acquiring essential driving knowledge through supervised fine-tuning (SFT) and further enhancing decision-making and planning via reinforcement fine-tuning (RFT), have shown strong potential in advancing the knowledge-driven autonomous driving (AD) paradigm. However, the learning nature of SFT still limits the generalization of reasoning, thereby constraining the full potential of driving performance. Meanwhile, current RFT approaches are primarily applied to downstream tasks, since scene understanding is an open-ended problem where corresponding rewards are difficult to quantify. To address these limitations, we propose OpenREAD, an OPEN-ended REasoning reinforced vision-language model (VLM)-based autonomous driving (AD) framework that enables end-to-end RFT across the full spectrum from high-level reasoning to low-level trajectory planning. Specifically, we begin by constructing large-scale Chain-of-Thought (CoT) annotations on open-source driving-related knowledge datasets, and employ the powerful Qwen3 large language model (LLM) as the critic in RFT to quantify reasoning quality for open-ended questions during reward modeling. Extensive experiments confirm that joint end-to-end RFT yields substantial improvements in both upstream and downstream tasks, enabling OpenREAD to achieve state-of-the-art performance on reasoning and planning benchmarks.
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            <a href="https://www.alphaxiv.org/abs/2512.01803v1" target="_blank" rel="noopener noreferrer">
                生成式动作叙事：评估合成视频中的人体运动
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            Generative Action Tell-Tales: Assessing Human Motion in Synthesized Videos
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xavier Thomas, Youngsun Lim, Ananya Srinivasan, Audrey Zheng, Deepti Ghadiyaram
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于合成视频中人体运动的评估，属于计算机视觉和视频生成领域。虽然涉及生成模型，但缺乏与推荐系统、搜索或广告领域的明确联系，也不涉及Transformer架构或LLM技术在这些领域的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 15:36:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01803v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01803v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Despite rapid advances in video generative models, robust metrics for evaluating visual and temporal correctness of complex human actions remain elusive. Critically, existing pure-vision encoders and Multimodal Large Language Models (MLLMs) are strongly appearance-biased, lack temporal understanding, and thus struggle to discern intricate motion dynamics and anatomical implausibilities in generated videos. We tackle this gap by introducing a novel evaluation metric derived from a learned latent space of real-world human actions. Our method first captures the nuances, constraints, and temporal smoothness of real-world motion by fusing appearance-agnostic human skeletal geometry features with appearance-based features. We posit that this combined feature space provides a robust representation of action plausibility. Given a generated video, our metric quantifies its action quality by measuring the distance between its underlying representations and this learned real-world action distribution. For rigorous validation, we develop a new multi-faceted benchmark specifically designed to probe temporally challenging aspects of human action fidelity. Through extensive experiments, we show that our metric achieves substantial improvement of more than 68% compared to existing state-of-the-art methods on our benchmark, performs competitively on established external benchmarks, and has a stronger correlation with human perception. Our in-depth analysis reveals critical limitations in current video generative models and establishes a new standard for advanced research in video generation.
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            <a href="https://www.alphaxiv.org/abs/2512.01789v1" target="_blank" rel="noopener noreferrer">
                SAM3-UNet：简化版Segment Anything Model 3适配
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            SAM3-UNet: Simplified Adaptation of Segment Anything Model 3
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinyu Xiong, Zihuang Wu, Lei Lu, Yufa Xia
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于计算机视觉领域的图像分割模型（Segment Anything Model）及其架构适配，属于纯粹的视觉技术范畴。虽然SAM模型本身是基础模型，但该论文的简化适配工作没有展示与推荐系统、搜索或广告领域的潜在应用连接，因此与当前关注点无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 15:27:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01789v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01789v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    In this paper, we introduce SAM3-UNet, a simplified variant of Segment Anything Model 3 (SAM3), designed to adapt SAM3 for downstream tasks at a low cost. Our SAM3-UNet consists of three components: a SAM3 image encoder, a simple adapter for parameter-efficient fine-tuning, and a lightweight U-Net-style decoder. Preliminary experiments on multiple tasks, such as mirror detection and salient object detection, demonstrate that the proposed SAM3-UNet outperforms the prior SAM2-UNet and other state-of-the-art methods, while requiring less than 6 GB of GPU memory during training with a batch size of 12. The code is publicly available at https://github.com/WZH0120/SAM3-UNet.
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            <a href="https://www.alphaxiv.org/abs/2512.01788v1" target="_blank" rel="noopener noreferrer">
                面向地球观测的习得图像压缩：对下游分割任务的启示
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            Learned Image Compression for Earth Observation: Implications for Downstream Segmentation Tasks
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Christian Mollière, Iker Cumplido, Marco Zeulner, Lukas Liesenhoff, Matthias Sch...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于地球观测领域的图像压缩技术及其对分割任务的影响，属于计算机视觉的特定应用领域。虽然涉及压缩技术，但缺乏与推荐系统、搜索或广告领域的明确关联，也未提及LLM、Transformer架构或异构数据处理等当前关注的核心技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 15:27:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01788v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01788v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The rapid growth of data from satellite-based Earth observation (EO) systems poses significant challenges in data transmission and storage. We evaluate the potential of task-specific learned compression algorithms in this context to reduce data volumes while retaining crucial information. In detail, we compare traditional compression (JPEG 2000) versus a learned compression approach (Discretized Mixed Gaussian Likelihood) on three EO segmentation tasks: Fire, cloud, and building detection. Learned compression notably outperforms JPEG 2000 for large-scale, multi-channel optical imagery in both reconstruction quality (PSNR) and segmentation accuracy. However, traditional codecs remain competitive on smaller, single-channel thermal infrared datasets due to limited data and architectural constraints. Additionally, joint end-to-end optimization of compression and segmentation models does not improve performance over standalone optimization.
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            <a href="https://www.alphaxiv.org/abs/2512.01774v1" target="_blank" rel="noopener noreferrer">
                评估SAM2在视频语义分割中的性能
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            Evaluating SAM2 for Video Semantic Segmentation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Syed Hesham Syed Ariff, Yun Liu, Guolei Sun, Jing Yang, Henghui Ding, Xue Geng, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于计算机视觉中的视频语义分割任务，属于纯粹的视觉领域研究。虽然SAM2可能涉及基础模型技术，但标题未表明其在推荐系统、搜索或广告领域的潜在应用，也没有涉及异构数据统一建模或Transformer架构进展等关注点。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 15:15:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01774v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01774v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The Segmentation Anything Model 2 (SAM2) has proven to be a powerful foundation model for promptable visual object segmentation in both images and videos, capable of storing object-aware memories and transferring them temporally through memory blocks. While SAM2 excels in video object segmentation by providing dense segmentation masks based on prompts, extending it to dense Video Semantic Segmentation (VSS) poses challenges due to the need for spatial accuracy, temporal consistency, and the ability to track multiple objects with complex boundaries and varying scales. This paper explores the extension of SAM2 for VSS, focusing on two primary approaches and highlighting firsthand observations and common challenges faced during this process. The first approach involves using SAM2 to extract unique objects as masks from a given image, with a segmentation network employed in parallel to generate and refine initial predictions. The second approach utilizes the predicted masks to extract unique feature vectors, which are then fed into a simple network for classification. The resulting classifications and masks are subsequently combined to produce the final segmentation. Our experiments suggest that leveraging SAM2 enhances overall performance in VSS, primarily due to its precise predictions of object boundaries.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01771v1" target="_blank" rel="noopener noreferrer">
                使用可学习边缘核进行稳健的刚性和非刚性医学图像配准
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Robust Rigid and Non-Rigid Medical Image Registration Using Learnable Edge Kernels
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ahsan Raza Siyal, Markus Haltmeier, Ruth Steiger, Malik Galijasevic, Elke Ruth G...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及医学图像配准，属于医学领域的特定应用，这直接违反了'Irrelevant Topics'中关于'Medical, Biology, Chemistry, Physics or other domain-specific applications'的排除规则。虽然提到了'learnable kernels'，但这在医学图像处理上下文中，与推荐系统、搜索或广告的核心技术焦点没有明确关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 15:13:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01771v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01771v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Medical image registration is crucial for various clinical and research applications including disease diagnosis or treatment planning which require alignment of images from different modalities, time points, or subjects. Traditional registration techniques often struggle with challenges such as contrast differences, spatial distortions, and modality-specific variations. To address these limitations, we propose a method that integrates learnable edge kernels with learning-based rigid and non-rigid registration techniques. Unlike conventional layers that learn all features without specific bias, our approach begins with a predefined edge detection kernel, which is then perturbed with random noise. These kernels are learned during training to extract optimal edge features tailored to the task. This adaptive edge detection enhances the registration process by capturing diverse structural features critical in medical imaging. To provide clearer insight into the contribution of each component in our design, we introduce four variant models for rigid registration and four variant models for non-rigid registration. We evaluated our approach using a dataset provided by the Medical University across three setups: rigid registration without skull removal, with skull removal, and non-rigid registration. Additionally, we assessed performance on two publicly available datasets. Across all experiments, our method consistently outperformed state-of-the-art techniques, demonstrating its potential to improve multi-modal image alignment and anatomical structure analysis.
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            <a href="https://www.alphaxiv.org/abs/2512.01769v1" target="_blank" rel="noopener noreferrer">
                VideoScoop：一种用于视频分析的非传统领域无关框架
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            VideoScoop: A Non-Traditional Domain-Independent Framework For Video Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hafsa Billah
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于视频分析这一计算机视觉领域，属于纯粹的视觉技术研究。虽然视频内容可能出现在推荐或搜索系统中，但该框架强调“领域无关”和“非传统”，表明其核心是通用视频处理而非针对RecSys/Search/Ads的特定应用。没有迹象表明该工作涉及LLM技术、Transformer架构、多模态建模或与推荐/搜索/广告系统的直接集成，因此与您关注的所有技术方向均不相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 15:09:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01769v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01769v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.DB</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Automatically understanding video contents is important for several applications in Civic Monitoring (CM), general Surveillance (SL), Assisted Living (AL), etc. Decades of Image and Video Analysis (IVA) research have advanced tasks such as content extraction (e.g., object recognition and tracking). Identifying meaningful activities or situations (e.g., two objects coming closer) remains difficult and cannot be achieved by content extraction alone. Currently, Video Situation Analysis (VSA) is done manually with a human in the loop, which is error-prone and labor-intensive, or through custom algorithms designed for specific video types or situations. These algorithms are not general-purpose and require a new algorithm/software for each new situation or video from a new domain. This report proposes a general-purpose VSA framework that overcomes the above limitations. Video contents are extracted once using state-of-the-art Video Content Extraction technologies. They are represented using two alternative models -- the extended relational model (R++) and graph models. When represented using R++, the extracted contents can be used as data streams, enabling Continuous Query Processing via the proposed Continuous Query Language for Video Analysis. The graph models complement this by enabling the detection of situations that are difficult or impossible to detect using the relational model alone. Existing graph algorithms and newly developed algorithms support a wide variety of situation detection. To support domain independence, primitive situation variants across domains are identified and expressed as parameterized templates. Extensive experiments were conducted across several interesting situations from three domains -- AL, CM, and SL-- to evaluate the accuracy, efficiency, and robustness of the proposed approach using a dataset of videos of varying lengths from these domains.
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            <a href="https://www.alphaxiv.org/abs/2512.01755v1" target="_blank" rel="noopener noreferrer">
                FreqEdit：通过保留高频特征实现鲁棒的多轮图像编辑
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            FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yucheng Liao, Jiajun Liang, Kaiqian Cui, Baoquan Zhao, Haoran Xie, Wei Liu, Qing...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向图像编辑技术，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术无关。虽然提到了多轮交互，但这仅限于图像编辑的上下文，没有涉及用户行为序列建模或异构数据处理，也不属于LLM或Transformer架构的进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 15:00:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01755v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01755v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Instruction-based image editing through natural language has emerged as a powerful paradigm for intuitive visual manipulation. While recent models achieve impressive results on single edits, they suffer from severe quality degradation under multi-turn editing. Through systematic analysis, we identify progressive loss of high-frequency information as the primary cause of this quality degradation. We present FreqEdit, a training-free framework that enables stable editing across 10+ consecutive iterations. Our approach comprises three synergistic components: (1) high-frequency feature injection from reference velocity fields to preserve fine-grained details, (2) an adaptive injection strategy that spatially modulates injection strength for precise region-specific control, and (3) a path compensation mechanism that periodically recalibrates the editing trajectory to prevent over-constraint. Extensive experiments demonstrate that FreqEdit achieves superior performance in both identity preservation and instruction following compared to seven state-of-the-art baselines.
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            <a href="https://www.alphaxiv.org/abs/2512.01687v1" target="_blank" rel="noopener noreferrer">
                重新审视直接编码：静态图像脉冲神经网络的可学习时序动态
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        <div class="mb-2 text-base text-gray-700">
            Revisiting Direct Encoding: Learnable Temporal Dynamics for Static Image Spiking Neural Networks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huaxu He
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于脉冲神经网络（SNN）在静态图像处理中的时序动态优化，属于计算神经科学和神经形态计算领域。虽然涉及神经网络架构，但脉冲神经网络与Transformer架构、LLM技术以及推荐/搜索/广告系统没有直接关联，也不符合对异构数据统一建模的关注方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 13:55:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01687v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01687v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.NE</span><span class="category-tag">cs.CV</span></div>
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                    Handling static images that lack inherent temporal dynamics remains a fundamental challenge for spiking neural networks (SNNs). In directly trained SNNs, static inputs are typically repeated across time steps, causing the temporal dimension to collapse into a rate like representation and preventing meaningful temporal modeling. This work revisits the reported performance gap between direct and rate based encodings and shows that it primarily stems from convolutional learnability and surrogate gradient formulations rather than the encoding schemes themselves. To illustrate this mechanism level clarification, we introduce a minimal learnable temporal encoding that adds adaptive phase shifts to induce meaningful temporal variation from static inputs.
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            <a href="https://www.alphaxiv.org/abs/2512.01681v1" target="_blank" rel="noopener noreferrer">
                在多中心间皮瘤活检数据上对基于切除训练的自监督模型进行跨域验证
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            Cross-Domain Validation of a Resection-Trained Self-Supervised Model on Multicentre Mesothelioma Biopsies
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Farzaneh Seyedshahi, Francesca Damiola, Sylvie Lantuejoul, Ke Yuan, John Le Ques...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及医学领域（间皮瘤活检）和特定医学训练方法（切除训练），这属于明确的无关主题。虽然提到了自监督模型，但其应用场景完全局限于医学诊断，与推荐系统、搜索或广告领域没有任何潜在关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 13:46:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01681v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01681v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Accurate subtype classification and outcome prediction in mesothelioma are essential for guiding therapy and patient care. Most computational pathology models are trained on large tissue images from resection specimens, limiting their use in real-world settings where small biopsies are common. We show that a self-supervised encoder trained on resection tissue can be applied to biopsy material, capturing meaningful morphological patterns. Using these patterns, the model can predict patient survival and classify tumor subtypes. This approach demonstrates the potential of AI-driven tools to support diagnosis and treatment planning in mesothelioma.
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            <a href="https://www.alphaxiv.org/abs/2512.01677v1" target="_blank" rel="noopener noreferrer">
                基于结构与接触感知表示的开集手-物交互视频生成
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            Open-world Hand-Object Interaction Video Generation Based on Structure and Contact-aware Representation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haodong Yan, Hang Yu, Zhide Zhong, Weilin Yuan, Xin Gong, Zehang Luo, Chengxi He...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于手-物交互视频生成，属于计算机视觉中的特定任务，与推荐系统、搜索或广告的核心技术无直接关联。虽然涉及多模态表示学习，但其应用场景（手部动作视频生成）与RecSys/Search/Ads的异构数据处理需求（如用户序列与上下文特征）没有明确的对应关系，且未提及任何潜在的应用转化路径。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 13:44:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01677v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01677v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Generating realistic hand-object interactions (HOI) videos is a significant challenge due to the difficulty of modeling physical constraints (e.g., contact and occlusion between hands and manipulated objects). Current methods utilize HOI representation as an auxiliary generative objective to guide video synthesis. However, there is a dilemma between 2D and 3D representations that cannot simultaneously guarantee scalability and interaction fidelity. To address this limitation, we propose a structure and contact-aware representation that captures hand-object contact, hand-object occlusion, and holistic structure context without 3D annotations. This interaction-oriented and scalable supervision signal enables the model to learn fine-grained interaction physics and generalize to open-world scenarios. To fully exploit the proposed representation, we introduce a joint-generation paradigm with a share-and-specialization strategy that generates interaction-oriented representations and videos. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on two real-world datasets in generating physics-realistic and temporally coherent HOI videos. Furthermore, our approach exhibits strong generalization to challenging open-world scenarios, highlighting the benefit of our scalable design. Our project page is https://hgzn258.github.io/SCAR/.
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            <a href="https://www.alphaxiv.org/abs/2512.01665v1" target="_blank" rel="noopener noreferrer">
                弥合尺度差距：遥感影像中平衡的微小与通用目标检测
            </a>
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            Bridging the Scale Gap: Balanced Tiny and General Object Detection in Remote Sensing Imagery
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhicheng Zhao, Yin Huang, Lingma Sun, Chenglong Li, Jin Tang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于遥感影像中的计算机视觉任务（目标检测），属于纯粹的视觉领域研究。虽然涉及多尺度处理技术，但论文标题明确限定在遥感应用场景，与推荐系统、搜索或广告的核心技术领域没有直接关联，也不涉及LLM、Transformer架构或异构数据处理等当前关注的技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 13:36:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01665v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01665v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Tiny object detection in remote sensing imagery has attracted significant research interest in recent years. Despite recent progress, achieving balanced detection performance across diverse object scales remains a formidable challenge, particularly in scenarios where dense tiny objects and large objects coexist. Although large foundation models have revolutionized general vision tasks, their application to tiny object detection remains unexplored due to the extreme scale variation and density distribution inherent to remote sensing imagery. To bridge this scale gap, we propose ScaleBridge-Det, to the best of our knowledge, the first large detection framework designed for tiny objects, which could achieve balanced performance across diverse scales through scale-adaptive expert routing and density-guided query allocation. Specifically, we introduce a Routing-Enhanced Mixture Attention (REM) module that dynamically selects and fuses scale-specific expert features via adaptive routing to address the tendency of standard MoE models to favor dominant scales. REM generates complementary and discriminative multi-scale representations suitable for both tiny and large objects. Furthermore, we present a Density-Guided Dynamic Query (DGQ) module that predicts object density to adaptively adjust query positions and numbers, enabling efficient resource allocation for objects of varying scales. The proposed framework allows ScaleBridge-Det to simultaneously optimize performance for both dense tiny and general objects without trade-offs. Extensive experiments on benchmark and cross-domain datasets demonstrate that ScaleBridge-Det achieves state-of-the-art performance on AI-TOD-V2 and DTOD, while exhibiting superior cross-domain robustness on VisDrone.
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            <a href="https://www.alphaxiv.org/abs/2512.01657v1" target="_blank" rel="noopener noreferrer">
                DB-KAUNet：一种用于视网膜血管分割的自适应双分支Kolmogorov-Arnold UNet
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            DB-KAUNet: An Adaptive Dual Branch Kolmogorov-Arnold UNet for Retinal Vessel Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hongyu Xu, Panpan Meng, Meng Wang, Dayu Hu, Liming Liang, Xiaoqi Sheng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分割（视网膜血管），属于明确的医学/生物领域应用，与RecSys/Search/Ads无关。虽然标题提到神经网络架构（UNet变体），但这是纯粹的计算机视觉应用，没有展示任何与推荐系统、搜索或广告相关的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 13:30:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01657v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01657v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Accurate segmentation of retinal vessels is crucial for the clinical diagnosis of numerous ophthalmic and systemic diseases. However, traditional Convolutional Neural Network (CNN) methods exhibit inherent limitations, struggling to capture long-range dependencies and complex nonlinear relationships. To address the above limitations, an Adaptive Dual Branch Kolmogorov-Arnold UNet (DB-KAUNet) is proposed for retinal vessel segmentation. In DB-KAUNet, we design a Heterogeneous Dual-Branch Encoder (HDBE) that features parallel CNN and Transformer pathways. The HDBE strategically interleaves standard CNN and Transformer blocks with novel KANConv and KAT blocks, enabling the model to form a comprehensive feature representation. To optimize feature processing, we integrate several critical components into the HDBE. First, a Cross-Branch Channel Interaction (CCI) module is embedded to facilitate efficient interaction of channel features between the parallel pathways. Second, an attention-based Spatial Feature Enhancement (SFE) module is employed to enhance spatial features and fuse the outputs from both branches. Building upon the SFE module, an advanced Spatial Feature Enhancement with Geometrically Adaptive Fusion (SFE-GAF) module is subsequently developed. In the SFE-GAF module, adaptive sampling is utilized to focus on true vessel morphology precisely. The adaptive process strengthens salient vascular features while significantly reducing background noise and computational overhead. Extensive experiments on the DRIVE, STARE, and CHASE_DB1 datasets validate that DB-KAUNet achieves leading segmentation performance and demonstrates exceptional robustness.
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            <a href="https://www.alphaxiv.org/abs/2512.01643v1" target="_blank" rel="noopener noreferrer">
                ViT$^3$：解锁视觉领域的测试时训练
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        <div class="mb-2 text-base text-gray-700">
            ViT$^3$: Unlocking Test-Time Training in Vision
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dongchen Han, Yining Li, Tianyu Li, Zixuan Cao, Ziming Wang, Jun Song, Yu Cheng,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于计算机视觉领域的测试时训练技术，属于纯粹的视觉研究范畴。虽然提到了ViT（Vision Transformer），但未涉及任何与推荐系统、搜索或广告相关的异构数据处理、多模态建模或实际应用场景，完全属于被排除的“纯粹视觉论文”类别。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 13:14:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01643v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01643v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Test-Time Training (TTT) has recently emerged as a promising direction for efficient sequence modeling. TTT reformulates attention operation as an online learning problem, constructing a compact inner model from key-value pairs at test time. This reformulation opens a rich and flexible design space while achieving linear computational complexity. However, crafting a powerful visual TTT design remains challenging: fundamental choices for the inner module and inner training lack comprehensive understanding and practical guidelines. To bridge this critical gap, in this paper, we present a systematic empirical study of TTT designs for visual sequence modeling. From a series of experiments and analyses, we distill six practical insights that establish design principles for effective visual TTT and illuminate paths for future improvement. These findings culminate in the Vision Test-Time Training (ViT$^3$) model, a pure TTT architecture that achieves linear complexity and parallelizable computation. We evaluate ViT$^3$ across diverse visual tasks, including image classification, image generation, object detection, and semantic segmentation. Results show that ViT$^3$ consistently matches or outperforms advanced linear-complexity models (e.g., Mamba and linear attention variants) and effectively narrows the gap to highly optimized vision Transformers. We hope this study and the ViT$^3$ baseline can facilitate future work on visual TTT models. Code is available at https://github.com/LeapLabTHU/ViTTT.
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            <a href="https://www.alphaxiv.org/abs/2512.01611v1" target="_blank" rel="noopener noreferrer">
                基于ShapeDTW的油基泥浆成像仪深度匹配方法
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            Depth Matching Method Based on ShapeDTW for Oil-Based Mud Imager
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fengfeng Li, Zhou Feng, Hongliang Wu, Hao Zhang, Han Tian, Peng Liu, Lixin Yuan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向石油工程领域的特定应用（油基泥浆成像仪），属于地质/石油工程领域，与推荐系统、搜索、广告或LLM技术完全无关。标题中的Depth Matching和ShapeDTW是特定领域的技术术语，没有显示出任何与当前关注领域的潜在联系。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 12:31:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01611v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01611v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">physics.geo-ph</span></div>
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                    In well logging operations using the oil-based mud (OBM) microresistivity imager, which employs an interleaved design with upper and lower pad sets, depth misalignment issues persist between the pad images even after velocity correction. This paper presents a depth matching method for borehole images based on the Shape Dynamic Time Warping (ShapeDTW) algorithm. The method extracts local shape features to construct a morphologically sensitive distance matrix, better preserving structural similarity between sequences during alignment. We implement this by employing a combined feature set of the one-dimensional Histogram of Oriented Gradients (HOG1D) and the original signal as the shape descriptor. Field test examples demonstrate that our method achieves precise alignment for images with complex textures, depth shifts, or local scaling. Furthermore, it provides a flexible framework for feature extension, allowing the integration of other descriptors tailored to specific geological features.
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            <a href="https://www.alphaxiv.org/abs/2512.01589v1" target="_blank" rel="noopener noreferrer">
                基于脓肿分割的头颈部CT影像内容索引与检索研究
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            Toward Content-based Indexing and Retrieval of Head and Neck CT with Abscess Segmentation
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Thao Thi Phuong Dao, Tan-Cong Nguyen, Trong-Le Do, Truong Hoang Viet, Nguyen Chi...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医学影像（头颈部CT）处理，属于医学/生物医学领域特定应用，与推荐系统、搜索或广告的核心技术无关。虽然涉及索引和检索概念，但这是针对医学影像的内容检索，而非通用信息检索或推荐系统技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 12:04:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01589v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01589v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Abscesses in the head and neck represent an acute infectious process that can potentially lead to sepsis or mortality if not diagnosed and managed promptly. Accurate detection and delineation of these lesions on imaging are essential for diagnosis, treatment planning, and surgical intervention. In this study, we introduce AbscessHeNe, a curated and comprehensively annotated dataset comprising 4,926 contrast-enhanced CT slices with clinically confirmed head and neck abscesses. The dataset is designed to facilitate the development of robust semantic segmentation models that can accurately delineate abscess boundaries and evaluate deep neck space involvement, thereby supporting informed clinical decision-making. To establish performance baselines, we evaluate several state-of-the-art segmentation architectures, including CNN, Transformer, and Mamba-based models. The highest-performing model achieved a Dice Similarity Coefficient of 0.39, Intersection-over-Union of 0.27, and Normalized Surface Distance of 0.67, indicating the challenges of this task and the need for further research. Beyond segmentation, AbscessHeNe is structured for future applications in content-based multimedia indexing and case-based retrieval. Each CT scan is linked with pixel-level annotations and clinical metadata, providing a foundation for building intelligent retrieval systems and supporting knowledge-driven clinical workflows. The dataset will be made publicly available at https://github.com/drthaodao3101/AbscessHeNe.git.
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            <a href="https://www.alphaxiv.org/abs/2512.01582v1" target="_blank" rel="noopener noreferrer">
                RoleMotion：面向细粒度描述下鲁棒场景特定角色扮演运动合成的大规模数据集
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            RoleMotion: A Large-Scale Dataset towards Robust Scene-Specific Role-Playing Motion Synthesis with Fine-grained Descriptions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junran Peng, Yiheng Huang, Silei Shen, Zeji Wei, Jingwei Yang, Baojie Wang, Yong...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于运动合成数据集，属于计算机视觉和图形学领域，与推荐系统、搜索或广告的核心技术无直接关联。标题中提到的“场景特定角色扮演运动”和“细粒度描述”主要涉及动作生成和视觉内容理解，不符合当前关注的LLM技术、Transformer架构进展或异构数据统一建模等方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 11:59:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01582v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01582v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this paper, we introduce RoleMotion, a large-scale human motion dataset that encompasses a wealth of role-playing and functional motion data tailored to fit various specific scenes. Existing text datasets are mainly constructed decentrally as amalgamation of assorted subsets that their data are nonfunctional and isolated to work together to cover social activities in various scenes. Also, the quality of motion data is inconsistent, and textual annotation lacks fine-grained details in these datasets. In contrast, RoleMotion is meticulously designed and collected with a particular focus on scenes and roles. The dataset features 25 classic scenes, 110 functional roles, over 500 behaviors, and 10296 high-quality human motion sequences of body and hands, annotated with 27831 fine-grained text descriptions. We build an evaluator stronger than existing counterparts, prove its reliability, and evaluate various text-to-motion methods on our dataset. Finally, we explore the interplay of motion generation of body and hands. Experimental results demonstrate the high-quality and functionality of our dataset on text-driven whole-body generation.
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            <a href="https://www.alphaxiv.org/abs/2512.01563v1" target="_blank" rel="noopener noreferrer">
                MasHeNe：一种基于窗口增强型Mamba与频域融合的头颈部CT肿块分割基准
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            MasHeNe: A Benchmark for Head and Neck CT Mass Segmentation using Window-Enhanced Mamba with Frequency-Domain Integration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Thao Thi Phuong Dao, Tan-Cong Nguyen, Nguyen Chi Thanh, Truong Hoang Viet, Trong...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医学影像（头颈部CT）分割任务，属于医学/生物医学领域特定应用，完全在'Irrelevant Topics'中明确排除的范围。虽然提到了Mamba架构（一种序列建模方法），但应用场景是医学图像分析而非推荐/搜索/广告系统，与当前关注的RecSys/Search/Ads核心领域无直接关联。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 11:38:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01563v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01563v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Head and neck masses are space-occupying lesions that can compress the airway and esophagus and may affect nerves and blood vessels. Available public datasets primarily focus on malignant lesions and often overlook other space-occupying conditions in this region. To address this gap, we introduce MasHeNe, an initial dataset of 3,779 contrast-enhanced CT slices that includes both tumors and cysts with pixel-level annotations. We also establish a benchmark using standard segmentation baselines and report common metrics to enable fair comparison. In addition, we propose the Windowing-Enhanced Mamba with Frequency integration (WEMF) model. WEMF applies tri-window enhancement to enrich the input appearance before feature extraction. It further uses multi-frequency attention to fuse information across skip connections within a U-shaped Mamba backbone. On MasHeNe, WEMF attains the best performance among evaluated methods, with a Dice of 70.45%, IoU of 66.89%, NSD of 72.33%, and HD95 of 5.12 mm. This model indicates stable and strong results on this challenging task. MasHeNe provides a benchmark for head-and-neck mass segmentation beyond malignancy-only datasets. The observed error patterns also suggest that this task remains challenging and requires further research. Our dataset and code are available at https://github.com/drthaodao3101/MasHeNe.git.
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            <a href="https://www.alphaxiv.org/abs/2512.01534v1" target="_blank" rel="noopener noreferrer">
                脑成像中的深度无监督异常检测：大规模基准测试与偏差分析
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            Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alexander Frotscher, Christian F. Baumgartner, Thomas Wolfers
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学领域（脑成像）的异常检测，属于明确的无关主题（医学/生物学应用）。虽然涉及深度学习技术，但缺乏任何与推荐系统、搜索或广告相关的潜在应用或技术迁移可能性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 11:03:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01534v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01534v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Deep unsupervised anomaly detection in brain magnetic resonance imaging offers a promising route to identify pathological deviations without requiring lesion-specific annotations. Yet, fragmented evaluations, heterogeneous datasets, and inconsistent metrics have hindered progress toward clinical translation. Here, we present a large-scale, multi-center benchmark of deep unsupervised anomaly detection for brain imaging. The training cohort comprised 2,976 T1 and 2,972 T2-weighted scans from healthy individuals across six scanners, with ages ranging from 6 to 89 years. Validation used 92 scans to tune hyperparameters and estimate unbiased thresholds. Testing encompassed 2,221 T1w and 1,262 T2w scans spanning healthy datasets and diverse clinical cohorts. Across all algorithms, the Dice-based segmentation performance varied between 0.03 and 0.65, indicating substantial variability. To assess robustness, we systematically evaluated the impact of different scanners, lesion types and sizes, as well as demographics (age, sex). Reconstruction-based methods, particularly diffusion-inspired approaches, achieved the strongest lesion segmentation performance, while feature-based methods showed greater robustness under distributional shifts. However, systematic biases, such as scanner-related effects, were observed for the majority of algorithms, including that small and low-contrast lesions were missed more often, and that false positives varied with age and sex. Increasing healthy training data yields only modest gains, underscoring that current unsupervised anomaly detection frameworks are limited algorithmically rather than by data availability. Our benchmark establishes a transparent foundation for future research and highlights priorities for clinical translation, including image native pretraining, principled deviation measures, fairness-aware modeling, and robust domain adaptation.
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            <a href="https://www.alphaxiv.org/abs/2512.01519v1" target="_blank" rel="noopener noreferrer">
                QuantumCanvas：用于原子相互作用视觉学习的多模态基准
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            QuantumCanvas: A Multimodal Benchmark for Visual Learning of Atomic Interactions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及量子物理和原子相互作用的视觉学习，这属于物理或化学等特定领域应用，与推荐系统、搜索或广告无关。标题中的'多模态'可能指视觉与科学数据的结合，但整体内容明显偏离了指定的技术焦点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 10:44:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01519v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01519v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cond-mat.mtrl-sci</span><span class="category-tag">quant-ph</span></div>
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                    Despite rapid advances in molecular and materials machine learning, most models still lack physical transferability: they fit correlations across whole molecules or crystals rather than learning the quantum interactions between atomic pairs. Yet bonding, charge redistribution, orbital hybridization, and electronic coupling all emerge from these two-body interactions that define local quantum fields in many-body systems. We introduce QuantumCanvas, a large-scale multimodal benchmark that treats two-body quantum systems as foundational units of matter. The dataset spans 2,850 element-element pairs, each annotated with 18 electronic, thermodynamic, and geometric properties and paired with ten-channel image representations derived from l- and m-resolved orbital densities, angular field transforms, co-occupancy maps, and charge-density projections. These physically grounded images encode spatial, angular, and electrostatic symmetries without explicit coordinates, providing an interpretable visual modality for quantum learning. Benchmarking eight architectures across 18 targets, we report mean absolute errors of 0.201 eV on energy gap using GATv2, 0.265 eV on HOMO and 0.274 eV on LUMO using EGNN. For energy-related quantities, DimeNet attains 2.27 eV total-energy MAE and 0.132 eV repulsive-energy MAE, while a multimodal fusion model achieves a 2.15 eV Mermin free-energy MAE. Pretraining on QuantumCanvas further improves convergence stability and generalization when fine-tuned on larger datasets such as QM9, MD17, and CrysMTM. By unifying orbital physics with vision-based representation learning, QuantumCanvas provides a principled and interpretable basis for learning transferable quantum interactions through coupled visual and numerical modalities. Dataset and model implementations are available at https://github.com/KurbanIntelligenceLab/QuantumCanvas.
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            <a href="https://www.alphaxiv.org/abs/2512.01510v1" target="_blank" rel="noopener noreferrer">
                面向医学图像分割领域泛化的语义感知随机卷积与源匹配方法
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            Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Franz Thaler, Martin Urschler, Mateusz Kozinski, Matthias AF Gsell, Gernot Plank...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分割的领域泛化，属于明确的医学领域特定应用，与用户关注的搜索、推荐、广告等商业系统无关。论文涉及的技术（语义感知随机卷积）可能具有计算机视觉背景，但缺乏向推荐/搜索/广告领域转化的明确路径或潜在应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 10:35:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01510v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01510v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    We tackle the challenging problem of single-source domain generalization (DG) for medical image segmentation. To this end, we aim for training a network on one domain (e.g., CT) and directly apply it to a different domain (e.g., MR) without adapting the model and without requiring images or annotations from the new domain during training. We propose a novel method for promoting DG when training deep segmentation networks, which we call SRCSM. During training, our method diversifies the source domain through semantic-aware random convolution, where different regions of a source image are augmented differently, based on their annotation labels. At test-time, we complement the randomization of the training domain via mapping the intensity of target domain images, making them similar to source domain data. We perform a comprehensive evaluation on a variety of cross-modality and cross-center generalization settings for abdominal, whole-heart and prostate segmentation, where we outperform previous DG techniques in a vast majority of experiments. Additionally, we also investigate our method when training on whole-heart CT or MR data and testing on the diastolic and systolic phase of cine MR data captured with different scanner hardware, where we make a step towards closing the domain gap in this even more challenging setting. Overall, our evaluation shows that SRCSM can be considered a new state-of-the-art in DG for medical image segmentation and, moreover, even achieves a segmentation performance that matches the performance of the in-domain baseline in several settings.
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            <a href="https://www.alphaxiv.org/abs/2512.01495v1" target="_blank" rel="noopener noreferrer">
                ELVIS：增强暗光环境下的视频实例分割
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            ELVIS: Enhance Low-Light for Video Instance Segmentation in the Dark
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Joanne Lin, Ruirui Lin, Yini Li, David Bull, Nantheera Anantrasirichai
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的暗光视频实例分割技术，属于纯粹的视觉处理领域。虽然标题中的“增强”可能暗示性能改进，但该技术主要针对视觉质量提升，与推荐系统、搜索、广告的排序、建模或LLM应用没有直接关联，也不涉及异构数据处理或Transformer架构创新。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 10:17:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01495v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01495v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Video instance segmentation (VIS) for low-light content remains highly challenging for both humans and machines alike, due to adverse imaging conditions including noise, blur and low-contrast. The lack of large-scale annotated datasets and the limitations of current synthetic pipelines, particularly in modeling temporal degradations, further hinder progress. Moreover, existing VIS methods are not robust to the degradations found in low-light videos and, as a result, perform poorly even when finetuned on low-light data. In this paper, we introduce \textbf{ELVIS} (\textbf{E}nhance \textbf{L}ow-light for \textbf{V}ideo \textbf{I}nstance \textbf{S}egmentation), a novel framework that enables effective domain adaptation of state-of-the-art VIS models to low-light scenarios. ELVIS comprises an unsupervised synthetic low-light video pipeline that models both spatial and temporal degradations, a calibration-free degradation profile synthesis network (VDP-Net) and an enhancement decoder head that disentangles degradations from content features. ELVIS improves performances by up to \textbf{+3.7AP} on the synthetic low-light YouTube-VIS 2019 dataset. Code will be released upon acceptance.
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            <a href="https://www.alphaxiv.org/abs/2512.01494v1" target="_blank" rel="noopener noreferrer">
                一种基于曲率依赖能量的曲线提取变分方法
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            A variational method for curve extraction with curvature-dependent energies
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Majid Arthaud, Antonin Chambolle, Vincent Duval
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉中的曲线提取和变分方法，属于纯粹的视觉处理技术。虽然提到了能量函数和曲率等数学概念，但未表明与推荐系统、搜索或广告有任何直接或间接的关联。标题内容完全属于您指定的无关主题范畴（Purely Vision papers without clear relevance to RecSys/Search/Ads）。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 10:16:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01494v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01494v1
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                    We introduce a variational approach for extracting curves between a list of possible endpoints, based on the discretization of an energy and Smirnov's decomposition theorem for vector fields. It is used to design a bi-level minimization approach to automatically extract curves and 1D structures from an image, which is mostly unsupervised. We extend then the method to curvature-dependent energies, using a now classical lifting of the curves in the space of positions and orientations equipped with an appropriate sub-Riemanian or Finslerian metric.
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            <a href="https://www.alphaxiv.org/abs/2512.01481v1" target="_blank" rel="noopener noreferrer">
                ChronosObserver：通过超空间扩散采样驯服四维世界
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            ChronosObserver: Taming 4D World with Hyperspace Diffusion Sampling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qisen Wang, Yifan Zhao, Peisen Shen, Jialu Li, Jia Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及4D世界建模和超空间扩散采样，这些概念主要与计算机视觉、3D重建或物理模拟相关，而非推荐系统、搜索或广告的核心技术。虽然扩散模型在生成式AI中有应用，但标题未表明与异构数据统一建模、Transformer架构改进或LLM在推荐/搜索中的直接应用相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 10:00:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01481v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01481v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Although prevailing camera-controlled video generation models can produce cinematic results, lifting them directly to the generation of 3D-consistent and high-fidelity time-synchronized multi-view videos remains challenging, which is a pivotal capability for taming 4D worlds. Some works resort to data augmentation or test-time optimization, but these strategies are constrained by limited model generalization and scalability issues. To this end, we propose ChronosObserver, a training-free method including World State Hyperspace to represent the spatiotemporal constraints of a 4D world scene, and Hyperspace Guided Sampling to synchronize the diffusion sampling trajectories of multiple views using the hyperspace. Experimental results demonstrate that our method achieves high-fidelity and 3D-consistent time-synchronized multi-view videos generation without training or fine-tuning for diffusion models.
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            <a href="https://www.alphaxiv.org/abs/2512.01478v1" target="_blank" rel="noopener noreferrer">
                CourtMotion：从骨骼数据中学习篮球事件驱动的运动表示
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            CourtMotion: Learning Event-Driven Motion Representations from Skeletal Data for Basketball
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Omer Sela, Michael Chertok, Lior Wolf
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于从骨骼数据中学习篮球运动的事件驱动表示，属于计算机视觉和运动分析领域。虽然涉及序列建模，但主要应用于体育分析，与推荐系统、搜索、广告或Transformer架构的核心进展没有直接关联。没有明确的潜在应用指向RecSys/Search/Ads领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 09:58:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01478v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01478v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.MA</span></div>
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                    This paper presents CourtMotion, a spatiotemporal modeling framework for analyzing and predicting game events and plays as they develop in professional basketball. Anticipating basketball events requires understanding both physical motion patterns and their semantic significance in the context of the game. Traditional approaches that use only player positions fail to capture crucial indicators such as body orientation, defensive stance, or shooting preparation motions. Our two-stage approach first processes skeletal tracking data through Graph Neural Networks to capture nuanced motion patterns, then employs a Transformer architecture with specialized attention mechanisms to model player interactions. We introduce event projection heads that explicitly connect player movements to basketball events like passes, shots, and steals, training the model to associate physical motion patterns with their tactical purposes. Experiments on NBA tracking data demonstrate significant improvements over position-only baselines: 35% reduction in trajectory prediction error compared to state-of-the-art position-based models and consistent performance gains across key basketball analytics tasks. The resulting pretrained model serves as a powerful foundation for multiple downstream tasks, with pick detection, shot taker identification, assist prediction, shot location classification, and shot type recognition demonstrating substantial improvements over existing methods.
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            <a href="https://www.alphaxiv.org/abs/2512.01444v1" target="_blank" rel="noopener noreferrer">
                FastAnimate：面向快速3D人体化身动画的可学习模板构建与姿态形变
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            FastAnimate: Towards Learnable Template Construction and Pose Deformation for Fast 3D Human Avatar Animation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jian Shu, Nanjie Yao, Gangjian Zhang, Junlong Ren, Yu Feng, Hao Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D人体动画生成，属于计算机图形学/视觉领域，与推荐系统、搜索或广告的核心技术无直接关联。其内容涉及3D建模、姿态形变等图形学技术，不符合当前关注的LLM、Transformer架构、推荐算法或异构数据统一建模等研究方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 09:28:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01444v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01444v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D human avatar animation aims at transforming a human avatar from an arbitrary initial pose to a specified target pose using deformation algorithms. Existing approaches typically divide this task into two stages: canonical template construction and target pose deformation. However, current template construction methods demand extensive skeletal rigging and often produce artifacts for specific poses. Moreover, target pose deformation suffers from structural distortions caused by Linear Blend Skinning (LBS), which significantly undermines animation realism. To address these problems, we propose a unified learning-based framework to address both challenges in two phases. For the former phase, to overcome the inefficiencies and artifacts during template construction, we leverage a U-Net architecture that decouples texture and pose information in a feed-forward process, enabling fast generation of a human template. For the latter phase, we propose a data-driven refinement technique that enhances structural integrity. Extensive experiments show that our model delivers consistent performance across diverse poses with an optimal balance between efficiency and quality,surpassing state-of-the-art (SOTA) methods.
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            <a href="https://www.alphaxiv.org/abs/2512.01424v1" target="_blank" rel="noopener noreferrer">
                《ViRectify：基于多模态大语言模型的视频推理修正挑战性基准》
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            \textit{ViRectify}: A Challenging Benchmark for Video Reasoning Correction with Multimodal Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xusen Hei, Jiali Chen, Jinyu Yang, Mengchen Zhao, Yi Cai
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于视频推理修正的基准测试，属于纯粹的视觉和多模态评估范畴。虽然涉及多模态大语言模型，但其应用场景（视频推理修正）与推荐系统、搜索或广告的核心技术需求（如排序、召回、用户建模等）无直接关联，也不符合您关注的LLM在RecSys/Search/Ads中的直接应用或基础技术进步方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 09:05:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01424v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01424v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    As multimodal large language models (MLLMs) frequently exhibit errors in complex video reasoning scenarios, correcting these errors is critical for uncovering their weaknesses and improving performance. However, existing benchmarks lack systematic evaluation of MLLMs' ability to identify and correct these video reasoning errors. To bridge this gap, we propose \textit{ViRectify}, a comprehensive benchmark to evaluate their fine-grained correction capability. Through an AI-assisted annotation pipeline with human verification, we construct a dataset of over 30\textit{K} instances spanning dynamic perception, scientific reasoning, and embodied decision-making domains. In \textit{ViRectify}, we challenge MLLMs to perform step-wise error identification and generate rationales with key video evidence grounding. In addition, we further propose the trajectory evidence-driven correction framework, comprising step-wise error trajectory and reward modeling on visual evidence-grounded correction. It encourages the model to explicitly concentrate on error propagation and key timestamps for correction. Extensive evaluation across 16 advanced MLLMs demonstrates that our \textit{ViRectify} serves as a challenging testbed, where GPT-5 achieves only 31.94\% correction accuracy. Our framework enables a Qwen2.5-VL-7B to consistently outperform the variants of 72B on \textit{ViRectify}, showing the effectiveness of our approach. Further analysis uncovers systematic asymmetries in error correction across models, and our dataset is also a valuable data resource to perform reflection learning. We believe \textit{ViRectify} provides a new direction for comprehensively evaluating the advanced MLLMs in video reasoning.
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            <a href="https://www.alphaxiv.org/abs/2512.01422v1" target="_blank" rel="noopener noreferrer">
                MDiff4STR：基于掩码扩散模型的场景文本识别
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            MDiff4STR: Mask Diffusion Model for Scene Text Recognition
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yongkun Du, Miaomiao Zhao, Songlin Fan, Zhineng Chen, Caiyan Jia, Yu-Gang Jiang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的场景文本识别任务，属于纯粹的视觉领域研究。虽然扩散模型是生成式AI的重要技术，但该论文没有展示与推荐系统、搜索或广告领域的明显关联，也不涉及异构数据建模或Transformer架构的进步。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 08:57:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01422v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01422v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Mask Diffusion Models (MDMs) have recently emerged as a promising alternative to auto-regressive models (ARMs) for vision-language tasks, owing to their flexible balance of efficiency and accuracy. In this paper, for the first time, we introduce MDMs into the Scene Text Recognition (STR) task. We show that vanilla MDM lags behind ARMs in terms of accuracy, although it improves recognition efficiency. To bridge this gap, we propose MDiff4STR, a Mask Diffusion model enhanced with two key improvement strategies tailored for STR. Specifically, we identify two key challenges in applying MDMs to STR: noising gap between training and inference, and overconfident predictions during inference. Both significantly hinder the performance of MDMs. To mitigate the first issue, we develop six noising strategies that better align training with inference behavior. For the second, we propose a token-replacement noise mechanism that provides a non-mask noise type, encouraging the model to reconsider and revise overly confident but incorrect predictions. We conduct extensive evaluations of MDiff4STR on both standard and challenging STR benchmarks, covering diverse scenarios including irregular, artistic, occluded, and Chinese text, as well as whether the use of pretraining. Across these settings, MDiff4STR consistently outperforms popular STR models, surpassing state-of-the-art ARMs in accuracy, while maintaining fast inference with only three denoising steps. Code: https://github.com/Topdu/OpenOCR.
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            <a href="https://www.alphaxiv.org/abs/2512.01390v1" target="_blank" rel="noopener noreferrer">
                FRAMER：基于扩散先验的频率对齐自适应调制自蒸馏方法，用于真实世界图像超分辨率
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            FRAMER: Frequency-Aligned Self-Distillation with Adaptive Modulation Leveraging Diffusion Priors for Real-World Image Super-Resolution
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Seungho Choi, Jeahun Sung, Jihyong Oh
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的图像超分辨率技术，属于纯粹的视觉处理任务。虽然提到了扩散模型，但应用场景仅限于图像增强，与推荐系统、搜索或广告的排名、建模、用户行为分析等核心问题没有直接关联。该技术没有展示出在异构数据处理、序列建模或推荐相关应用中的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 08:09:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01390v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01390v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Real-image super-resolution (Real-ISR) seeks to recover HR images from LR inputs with mixed, unknown degradations. While diffusion models surpass GANs in perceptual quality, they under-reconstruct high-frequency (HF) details due to a low-frequency (LF) bias and a depth-wise "low-first, high-later" hierarchy. We introduce FRAMER, a plug-and-play training scheme that exploits diffusion priors without changing the backbone or inference. At each denoising step, the final-layer feature map teaches all intermediate layers. Teacher and student feature maps are decomposed into LF/HF bands via FFT masks to align supervision with the model's internal frequency hierarchy. For LF, an Intra Contrastive Loss (IntraCL) stabilizes globally shared structure. For HF, an Inter Contrastive Loss (InterCL) sharpens instance-specific details using random-layer and in-batch negatives. Two adaptive modulators, Frequency-based Adaptive Weight (FAW) and Frequency-based Alignment Modulation (FAM), reweight per-layer LF/HF signals and gate distillation by current similarity. Across U-Net and DiT backbones (e.g., Stable Diffusion 2, 3), FRAMER consistently improves PSNR/SSIM and perceptual metrics (LPIPS, NIQE, MANIQA, MUSIQ). Ablations validate the final-layer teacher and random-layer negatives.
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            <a href="https://www.alphaxiv.org/abs/2512.01383v1" target="_blank" rel="noopener noreferrer">
                PointNet4D：用于机器人应用中在线和离线感知的轻量级4D点云视频骨干网络
            </a>
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        <div class="mb-2 text-base text-gray-700">
            PointNet4D: A Lightweight 4D Point Cloud Video Backbone for Online and Offline Perception in Robotic Applications
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunze Liu, Zifan Wang, Peiran Wu, Jiayang Ao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于机器人应用中的4D点云视频处理，属于纯粹的计算机视觉和机器人感知领域。虽然提到了“在线和离线”处理，但这与推荐系统、搜索或广告中的实时/离线处理没有直接关联。该论文没有涉及任何与推荐系统、搜索、广告相关的技术，也不属于Transformer架构、LLM技术或异构数据统一建模的范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 07:58:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01383v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01383v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Understanding dynamic 4D environments-3D space evolving over time-is critical for robotic and interactive systems. These applications demand systems that can process streaming point cloud video in real-time, often under resource constraints, while also benefiting from past and present observations when available. However, current 4D backbone networks rely heavily on spatiotemporal convolutions and Transformers, which are often computationally intensive and poorly suited to real-time applications. We propose PointNet4D, a lightweight 4D backbone optimized for both online and offline settings. At its core is a Hybrid Mamba-Transformer temporal fusion block, which integrates the efficient state-space modeling of Mamba and the bidirectional modeling power of Transformers. This enables PointNet4D to handle variable-length online sequences efficiently across different deployment scenarios. To enhance temporal understanding, we introduce 4DMAP, a frame-wise masked auto-regressive pretraining strategy that captures motion cues across frames. Our extensive evaluations across 9 tasks on 7 datasets, demonstrating consistent improvements across diverse domains. We further demonstrate PointNet4D's utility by building two robotic application systems: 4D Diffusion Policy and 4D Imitation Learning, achieving substantial gains on the RoboTwin and HandoverSim benchmarks.
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            <a href="https://www.alphaxiv.org/abs/2512.01382v1" target="_blank" rel="noopener noreferrer">
                用于免训练示例引导图像编辑的可逆反演
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Reversible Inversion for Training-Free Exemplar-guided Image Editing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuke Li, Lianli Gao, Ji Zhang, Pengpeng Zeng, Lichuan Xiang, Hongkai Wen, Heng T...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的图像编辑技术，涉及可逆反演和示例引导方法，属于纯粹的视觉处理范畴。虽然标题提到“免训练”，但这与推荐系统、搜索或广告中的LLM应用、Transformer架构进展或异构数据统一建模没有直接关联，也不属于核心推荐/搜索/广告领域的进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 07:56:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01382v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01382v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Exemplar-guided Image Editing (EIE) aims to modify a source image according to a visual reference. Existing approaches often require large-scale pre-training to learn relationships between the source and reference images, incurring high computational costs. As a training-free alternative, inversion techniques can be used to map the source image into a latent space for manipulation. However, our empirical study reveals that standard inversion is sub-optimal for EIE, leading to poor quality and inefficiency. To tackle this challenge, we introduce \textbf{Reversible Inversion ({ReInversion})} for effective and efficient EIE. Specifically, ReInversion operates as a two-stage denoising process, which is first conditioned on the source image and subsequently on the reference. Besides, we introduce a Mask-Guided Selective Denoising (MSD) strategy to constrain edits to target regions, preserving the structural consistency of the background. Both qualitative and quantitative comparisons demonstrate that our ReInversion method achieves state-of-the-art EIE performance with the lowest computational overhead.
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            <a href="https://www.alphaxiv.org/abs/2512.01380v1" target="_blank" rel="noopener noreferrer">
                纹理化几何评估：基于3D潜在几何网络的感知式3D纹理形状度量
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        <div class="mb-2 text-base text-gray-700">
            Textured Geometry Evaluation: Perceptual 3D Textured Shape Metric via 3D Latent-Geometry Network
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianyu Luan, Xuelu Feng, Zixin Zhu, Phani Nuney, Sheng Liu, Xuan Gong, David Doe...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D纹理形状的感知评估，属于纯粹的计算机视觉/图形学领域。虽然提到了3D几何和潜在表示，但论文明确关注3D形状的感知质量评估，这与推荐系统、搜索或广告的核心技术（如排序、检索、用户建模）没有直接关联。即使考虑潜在应用，其3D图形处理焦点也超出了指定的技术领域范围。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 07:53:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01380v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01380v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Textured high-fidelity 3D models are crucial for games, AR/VR, and film, but human-aligned evaluation methods still fall behind despite recent advances in 3D reconstruction and generation. Existing metrics, such as Chamfer Distance, often fail to align with how humans evaluate the fidelity of 3D shapes. Recent learning-based metrics attempt to improve this by relying on rendered images and 2D image quality metrics. However, these approaches face limitations due to incomplete structural coverage and sensitivity to viewpoint choices. Moreover, most methods are trained on synthetic distortions, which differ significantly from real-world distortions, resulting in a domain gap. To address these challenges, we propose a new fidelity evaluation method that is based directly on 3D meshes with texture, without relying on rendering. Our method, named Textured Geometry Evaluation TGE, jointly uses the geometry and color information to calculate the fidelity of the input textured mesh with comparison to a reference colored shape. To train and evaluate our metric, we design a human-annotated dataset with real-world distortions. Experiments show that TGE outperforms rendering-based and geometry-only methods on real-world distortion dataset.
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            <a href="https://www.alphaxiv.org/abs/2512.01373v1" target="_blank" rel="noopener noreferrer">
                SRAM：用于无参考三维形状评估的形状真实感对齐度量
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            SRAM: Shape-Realism Alignment Metric for No Reference 3D Shape Evaluation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sheng Liu, Tianyu Luan, Phani Nuney, Xuelu Feng, Junsong Yuan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于三维形状评估的特定度量方法，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。论文标题中提到的'3D Shape Evaluation'明确指向视觉领域，没有暗示任何在RecSys/Search/Ads中的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 07:40:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01373v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01373v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D generation and reconstruction techniques have been widely used in computer games, film, and other content creation areas. As the application grows, there is a growing demand for 3D shapes that look truly realistic. Traditional evaluation methods rely on a ground truth to measure mesh fidelity. However, in many practical cases, a shape's realism does not depend on having a ground truth reference. In this work, we propose a Shape-Realism Alignment Metric that leverages a large language model (LLM) as a bridge between mesh shape information and realism evaluation. To achieve this, we adopt a mesh encoding approach that converts 3D shapes into the language token space. A dedicated realism decoder is designed to align the language model's output with human perception of realism. Additionally, we introduce a new dataset, RealismGrading, which provides human-annotated realism scores without the need for ground truth shapes. Our dataset includes shapes generated by 16 different algorithms on over a dozen objects, making it more representative of practical 3D shape distributions. We validate our metric's performance and generalizability through k-fold cross-validation across different objects. Experimental results show that our metric correlates well with human perceptions and outperforms existing methods, and has good generalizability.
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            <a href="https://www.alphaxiv.org/abs/2512.01366v1" target="_blank" rel="noopener noreferrer">
                BlinkBud：通过单耳塞上的采样单目3D检测从后方探测危险
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            BlinkBud: Detecting Hazards from Behind via Sampled Monocular 3D Detection on a Single Earbud
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunzhe Li, Jiajun Yan, Yuzhou Wei, Kechen Liu, Yize Zhao, Chong Zhang, Hongzi Zh...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉（单目3D检测）和可穿戴设备（耳塞）在安全应用中的使用，这属于纯粹的视觉或硬件领域，与推荐系统、搜索或广告的核心技术、LLM应用、Transformer架构或异构数据统一建模没有直接关联。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 07:25:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01366v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01366v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.HC</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Failing to be aware of speeding vehicles approaching from behind poses a huge threat to the road safety of pedestrians and cyclists. In this paper, we propose BlinkBud, which utilizes a single earbud and a paired phone to online detect hazardous objects approaching from behind of a user. The core idea is to accurately track visually identified objects utilizing a small number of sampled camera images taken from the earbud. To minimize the power consumption of the earbud and the phone while guaranteeing the best tracking accuracy, a novel 3D object tracking algorithm is devised, integrating both a Kalman filter based trajectory estimation scheme and an optimal image sampling strategy based on reinforcement learning. Moreover, the impact of constant user head movements on the tracking accuracy is significantly eliminated by leveraging the estimated pitch and yaw angles to correct the object depth estimation and align the camera coordinate system to the user's body coordinate system, respectively. We implement a prototype BlinkBud system and conduct extensive real-world experiments. Results show that BlinkBud is lightweight with ultra-low mean power consumptions of 29.8 mW and 702.6 mW on the earbud and smartphone, respectively, and can accurately detect hazards with a low average false positive ratio (FPR) and false negative ratio (FNR) of 4.90% and 1.47%, respectively.
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            <a href="https://www.alphaxiv.org/abs/2512.01352v1" target="_blank" rel="noopener noreferrer">
                OpenBox：三维空间中的任意边界框标注
            </a>
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        <div class="mb-2 text-base text-gray-700">
            OpenBox: Annotate Any Bounding Boxes in 3D
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>In-Jae Lee, Mungyeom Kim, Kwonyoung Ryu, Pierre Musacchio, Jaesik Park
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及3D视觉中的边界框标注技术，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术无直接关联。虽然3D视觉在某些特定场景（如AR购物）可能有间接应用，但论文本身并未体现对推荐/搜索/广告领域的明确技术贡献或应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 07:04:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01352v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01352v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Unsupervised and open-vocabulary 3D object detection has recently gained attention, particularly in autonomous driving, where reducing annotation costs and recognizing unseen objects are critical for both safety and scalability. However, most existing approaches uniformly annotate 3D bounding boxes, ignore objects' physical states, and require multiple self-training iterations for annotation refinement, resulting in suboptimal quality and substantial computational overhead. To address these challenges, we propose OpenBox, a two-stage automatic annotation pipeline that leverages a 2D vision foundation model. In the first stage, OpenBox associates instance-level cues from 2D images processed by a vision foundation model with the corresponding 3D point clouds via cross-modal instance alignment. In the second stage, it categorizes instances by rigidity and motion state, then generates adaptive bounding boxes with class-specific size statistics. As a result, OpenBox produces high-quality 3D bounding box annotations without requiring self-training. Experiments on the Waymo Open Dataset, the Lyft Level 5 Perception dataset, and the nuScenes dataset demonstrate improved accuracy and efficiency over baselines.
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            <a href="https://www.alphaxiv.org/abs/2512.01348v1" target="_blank" rel="noopener noreferrer">
                低资源语言手写文本识别
            </a>
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            Handwritten Text Recognition for Low Resource Languages
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sayantan Dey, Alireza Alaei, Partha Pratim Roy
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于低资源语言的手写文本识别，属于计算机视觉和OCR领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及文本处理，但主要针对特定领域的手写识别问题，没有展示出在推荐、搜索或广告中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 07:01:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01348v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01348v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Despite considerable progress in handwritten text recognition, paragraph-level handwritten text recognition, especially in low-resource languages, such as Hindi, Urdu and similar scripts, remains a challenging problem. These languages, often lacking comprehensive linguistic resources, require special attention to develop robust systems for accurate optical character recognition (OCR). This paper introduces BharatOCR, a novel segmentation-free paragraph-level handwritten Hindi and Urdu text recognition. We propose a ViT-Transformer Decoder-LM architecture for handwritten text recognition, where a Vision Transformer (ViT) extracts visual features, a Transformer decoder generates text sequences, and a pre-trained language model (LM) refines the output to improve accuracy, fluency, and coherence. Our model utilizes a Data-efficient Image Transformer (DeiT) model proposed for masked image modeling in this research work. In addition, we adopt a RoBERTa architecture optimized for masked language modeling (MLM) to enhance the linguistic comprehension and generative capabilities of the proposed model. The transformer decoder generates text sequences from visual embeddings. This model is designed to iteratively process a paragraph image line by line, called implicit line segmentation. The proposed model was evaluated using our custom dataset ('Parimal Urdu') and ('Parimal Hindi'), introduced in this research work, as well as two public datasets. The proposed model achieved benchmark results in the NUST-UHWR, PUCIT-OUHL, and Parimal-Urdu datasets, achieving character recognition rates of 96.24%, 92.05%, and 94.80%, respectively. The model also provided benchmark results using the Hindi dataset achieving a character recognition rate of 80.64%. The results obtained from our proposed model indicated that it outperformed several state-of-the-art Urdu text recognition methods.
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            <a href="https://www.alphaxiv.org/abs/2512.01340v1" target="_blank" rel="noopener noreferrer">
                EvalTalker：学习评估真实肖像驱动的多主体说话人像
            </a>
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        <div class="mb-2 text-base text-gray-700">
            EvalTalker: Learning to Evaluate Real-Portrait-Driven Multi-Subject Talking Humans
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yingjie Zhou, Xilei Zhu, Siyu Ren, Ziyi Zhao, Ziwen Wang, Farong Wen, Yu Zhou, J...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及多主体说话人像的评估，属于计算机视觉和语音合成交叉领域，与推荐系统、搜索或广告的核心技术无直接关联。标题中提到的“真实肖像驱动”和“多主体说话人像”表明其专注于视觉和语音生成质量评估，属于纯粹的视觉/语音应用范畴，不符合当前关注的任何技术方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:56:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01340v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01340v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Speech-driven Talking Human (TH) generation, commonly known as "Talker," currently faces limitations in multi-subject driving capabilities. Extending this paradigm to "Multi-Talker," capable of animating multiple subjects simultaneously, introduces richer interactivity and stronger immersion in audiovisual communication. However, current Multi-Talkers still exhibit noticeable quality degradation caused by technical limitations, resulting in suboptimal user experiences. To address this challenge, we construct THQA-MT, the first large-scale Multi-Talker-generated Talking Human Quality Assessment dataset, consisting of 5,492 Multi-Talker-generated THs (MTHs) from 15 representative Multi-Talkers using 400 real portraits collected online. Through subjective experiments, we analyze perceptual discrepancies among different Multi-Talkers and identify 12 common types of distortion. Furthermore, we introduce EvalTalker, a novel TH quality assessment framework. This framework possesses the ability to perceive global quality, human characteristics, and identity consistency, while integrating Qwen-Sync to perceive multimodal synchrony. Experimental results demonstrate that EvalTalker achieves superior correlation with subjective scores, providing a robust foundation for future research on high-quality Multi-Talker generation and evaluation.
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            <a href="https://www.alphaxiv.org/abs/2512.01334v1" target="_blank" rel="noopener noreferrer">
                AlignVid：文本引导图像到视频生成中语义保真度的免训练注意力缩放
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        <div class="mb-2 text-base text-gray-700">
            AlignVid: Training-Free Attention Scaling for Semantic Fidelity in Text-Guided Image-to-Video Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yexin Liu, Wen-Jie Shu, Zile Huang, Haoze Zheng, Yueze Wang, Manyuan Zhang, Ser-...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于文本引导的图像到视频生成技术，属于纯粹的视觉内容生成领域。虽然涉及注意力机制，但其应用场景（图像到视频生成）与推荐系统、搜索或广告的排名任务没有直接关联，也不符合将异构数据作为不同模态进行统一建模的VLM类比方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:53:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01334v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01334v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Text-guided image-to-video (TI2V) generation has recently achieved remarkable progress, particularly in maintaining subject consistency and temporal coherence. However, existing methods still struggle to adhere to fine-grained prompt semantics, especially when prompts entail substantial transformations of the input image (e.g., object addition, deletion, or modification), a shortcoming we term semantic negligence. In a pilot study, we find that applying a Gaussian blur to the input image improves semantic adherence. Analyzing attention maps, we observe clearer foreground-background separation. From an energy perspective, this corresponds to a lower-entropy cross-attention distribution. Motivated by this, we introduce AlignVid, a training-free framework with two components: (i) Attention Scaling Modulation (ASM), which directly reweights attention via lightweight Q or K scaling, and (ii) Guidance Scheduling (GS), which applies ASM selectively across transformer blocks and denoising steps to reduce visual quality degradation. This minimal intervention improves prompt adherence while limiting aesthetic degradation. In addition, we introduce OmitI2V to evaluate semantic negligence in TI2V generation, comprising 367 human-annotated samples that span addition, deletion, and modification scenarios. Extensive experiments demonstrate that AlignVid can enhance semantic fidelity.
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            <a href="https://www.alphaxiv.org/abs/2512.01333v1" target="_blank" rel="noopener noreferrer">
                优化中风风险预测：结合ROS平衡集成与可解释人工智能的机器学习流程
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            Optimizing Stroke Risk Prediction: A Machine Learning Pipeline Combining ROS-Balanced Ensembles and XAI
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>A S M Ahsanul Sarkar Akib, Raduana Khawla, Abdul Hasib
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医疗领域的风险预测（中风风险），属于明确的医疗/生物学应用范畴，这被列为不相关主题。虽然提到了机器学习流程和可解释AI（XAI），但其核心应用领域（医疗诊断）与推荐系统、搜索或广告的技术焦点完全无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:53:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01333v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01333v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Stroke is a major cause of death and permanent impairment, making it a major worldwide health concern. For prompt intervention and successful preventative tactics, early risk assessment is essential. To address this challenge, we used ensemble modeling and explainable AI (XAI) techniques to create an interpretable machine learning framework for stroke risk prediction. A thorough evaluation of 10 different machine learning models using 5-fold cross-validation across several datasets was part of our all-inclusive strategy, which also included feature engineering and data pretreatment (using Random Over-Sampling (ROS) to solve class imbalance). Our optimized ensemble model (Random Forest + ExtraTrees + XGBoost) performed exceptionally well, obtaining a strong 99.09% accuracy on the Stroke Prediction Dataset (SPD). We improved the model's transparency and clinical applicability by identifying three important clinical variables using LIME-based interpretability analysis: age, hypertension, and glucose levels. Through early prediction, this study highlights how combining ensemble learning with explainable AI (XAI) can deliver highly accurate and interpretable stroke risk assessment. By enabling data-driven prevention and personalized clinical decisions, our framework has the potential to transform stroke prediction and cardiovascular risk management.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01329v1" target="_blank" rel="noopener noreferrer">
                TagSplat：用于动态网格建模与跟踪的拓扑感知高斯泼溅
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            TagSplat: Topology-Aware Gaussian Splatting for Dynamic Mesh Modeling and Tracking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hanzhi Guo, Dongdong Weng, Mo Su, Yixiao Chen, Xiaonuo Dongye, Chenyu Xu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机图形学中的动态网格建模与跟踪技术，属于3D视觉和图形学领域。虽然提到了建模和跟踪，但未表明与推荐系统、搜索或广告有任何直接关联，也不属于LLM、Transformer架构或异构数据处理等关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:41:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01329v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01329v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.GR</span><span class="category-tag">cs.CV</span></div>
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                    Topology-consistent dynamic model sequences are essential for applications such as animation and model editing. However, existing 4D reconstruction methods face challenges in generating high-quality topology-consistent meshes. To address this, we propose a topology-aware dynamic reconstruction framework based on Gaussian Splatting. We introduce a Gaussian topological structure that explicitly encodes spatial connectivity. This structure enables topology-aware densification and pruning, preserving the manifold consistency of the Gaussian representation. Temporal regularization terms further ensure topological coherence over time, while differentiable mesh rasterization improves mesh quality. Experimental results demonstrate that our method reconstructs topology-consistent mesh sequences with significantly higher accuracy than existing approaches. Moreover, the resulting meshes enable precise 3D keypoint tracking. Project page: https://haza628.github.io/tagSplat/
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            <a href="https://www.alphaxiv.org/abs/2512.01324v1" target="_blank" rel="noopener noreferrer">
                Panda：用于高能物理的可复用传感器级表征的自蒸馏方法
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            Panda: Self-distillation of Reusable Sensor-level Representations for High Energy Physics
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Samuel Young, Kazuhiro Terao
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及高能物理学领域，这属于您指定的无关主题中的“Medical, Biology, Chemistry, Physics or other domain-specific applications”。标题中的“High Energy Physics”直接表明其研究背景与推荐系统、搜索或广告领域无关。尽管“self-distillation”是一种模型压缩技术，但论文将其应用于特定物理领域，没有提供任何与您关注领域相关的潜在应用或联系。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:28:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01324v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01324v1
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                <div class="flex flex-wrap"><span class="category-tag">hep-ex</span><span class="category-tag">cs.CV</span></div>
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                    Liquid argon time projection chambers (LArTPCs) provide dense, high-fidelity 3D measurements of particle interactions and underpin current and future neutrino and rare-event experiments. Physics reconstruction typically relies on complex detector-specific pipelines that use tens of hand-engineered pattern recognition algorithms or cascades of task-specific neural networks that require extensive, labeled simulation that requires a careful, time-consuming calibration process. We introduce \textbf{Panda}, a model that learns reusable sensor-level representations directly from raw unlabeled LArTPC data. Panda couples a hierarchical sparse 3D encoder with a multi-view, prototype-based self-distillation objective. On a simulated dataset, Panda substantially improves label efficiency and reconstruction quality, beating the previous state-of-the-art semantic segmentation model with 1,000$\times$ fewer labels. We also show that a single set-prediction head 1/20th the size of the backbone with no physical priors trained on frozen outputs from Panda can result in particle identification that is comparable with state-of-the-art (SOTA) reconstruction tools. Full fine-tuning further improves performance across all tasks.
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            <a href="https://www.alphaxiv.org/abs/2512.01319v1" target="_blank" rel="noopener noreferrer">
                重新思考颅内动脉瘤血管分割：从计算流体动力学应用的角度
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            Rethinking Intracranial Aneurysm Vessel Segmentation: A Perspective from Computational Fluid Dynamics Applications
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Feiyang Xiao, Yichi Zhang, Xigui Li, Yuanye Zhou, Chen Jiang, Xin Guo, Limei Han...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及医学领域（颅内动脉瘤）和计算流体动力学应用，这属于明确的无关主题（医学/生物学领域特定应用）。该内容与推荐系统、搜索、广告、LLM技术或Transformer架构没有任何关联，也没有展示出对这些领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:23:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01319v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01319v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    The precise segmentation of intracranial aneurysms and their parent vessels (IA-Vessel) is a critical step for hemodynamic analyses, which mainly depends on computational fluid dynamics (CFD). However, current segmentation methods predominantly focus on image-based evaluation metrics, often neglecting their practical effectiveness in subsequent CFD applications. To address this deficiency, we present the Intracranial Aneurysm Vessel Segmentation (IAVS) dataset, the first comprehensive, multi-center collection comprising 641 3D MRA images with 587 annotations of aneurysms and IA-Vessels. In addition to image-mask pairs, IAVS dataset includes detailed hemodynamic analysis outcomes, addressing the limitations of existing datasets that neglect topological integrity and CFD applicability. To facilitate the development and evaluation of clinically relevant techniques, we construct two evaluation benchmarks including global localization of aneurysms (Stage I) and fine-grained segmentation of IA-Vessel (Stage II) and develop a simple and effective two-stage framework, which can be used as a out-of-the-box method and strong baseline. For comprehensive evaluation of applicability of segmentation results, we establish a standardized CFD applicability evaluation system that enables the automated and consistent conversion of segmentation masks into CFD models, offering an applicability-focused assessment of segmentation outcomes. The dataset, code, and model will be public available at https://github.com/AbsoluteResonance/IAVS.
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            <a href="https://www.alphaxiv.org/abs/2512.01315v1" target="_blank" rel="noopener noreferrer">
                FOD-S2R：基于模拟到真实迁移学习的物体检测FOD数据集
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            FOD-S2R: A FOD Dataset for Sim2Real Transfer Learning based Object Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ashish Vashist, Qiranul Saadiyean, Suresh Sundaram, Chandra Sekhar Seelamantula
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向计算机视觉领域的物体检测任务，专注于模拟到真实迁移学习的数据集创建。这属于纯粹的视觉研究范畴，与推荐系统、搜索或广告的核心技术焦点没有直接关联，也没有展示出在异构数据处理或Transformer架构方面的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:16:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01315v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01315v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Foreign Object Debris (FOD) within aircraft fuel tanks presents critical safety hazards including fuel contamination, system malfunctions, and increased maintenance costs. Despite the severity of these risks, there is a notable lack of dedicated datasets for the complex, enclosed environments found inside fuel tanks. To bridge this gap, we present a novel dataset, FOD-S2R, composed of real and synthetic images of the FOD within a simulated aircraft fuel tank. Unlike existing datasets that focus on external or open-air environments, our dataset is the first to systematically evaluate the effectiveness of synthetic data in enhancing the real-world FOD detection performance in confined, closed structures. The real-world subset consists of 3,114 high-resolution HD images captured in a controlled fuel tank replica, while the synthetic subset includes 3,137 images generated using Unreal Engine. The dataset is composed of various Field of views (FOV), object distances, lighting conditions, color, and object size. Prior research has demonstrated that synthetic data can reduce reliance on extensive real-world annotations and improve the generalizability of vision models. Thus, we benchmark several state-of-the-art object detection models and demonstrate that introducing synthetic data improves the detection accuracy and generalization to real-world conditions. These experiments demonstrate the effectiveness of synthetic data in enhancing the model performance and narrowing the Sim2Real gap, providing a valuable foundation for developing automated FOD detection systems for aviation maintenance.
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                TokenPure：通过标记化外观与结构引导实现水印去除
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            TokenPure: Watermark Removal through Tokenized Appearance and Structural Guidance
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pei Yang, Yepeng Liu, Kelly Peng, Yuan Gao, Yiren Song
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于水印去除技术，属于计算机视觉中的图像处理领域。虽然提到了“Tokenized”可能涉及标记化处理，但核心应用是水印去除而非推荐系统、搜索或广告相关的建模、排名或理解任务。根据用户指定的无关主题列表，这属于“Purely Vision... papers without clear relevance to RecSys/Search/Ads”，与所有当前关注点均无直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:15:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01314v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01314v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    In the digital economy era, digital watermarking serves as a critical basis for ownership proof of massive replicable content, including AI-generated and other virtual assets. Designing robust watermarks capable of withstanding various attacks and processing operations is even more paramount. We introduce TokenPure, a novel Diffusion Transformer-based framework designed for effective and consistent watermark removal. TokenPure solves the trade-off between thorough watermark destruction and content consistency by leveraging token-based conditional reconstruction. It reframes the task as conditional generation, entirely bypassing the initial watermark-carrying noise. We achieve this by decomposing the watermarked image into two complementary token sets: visual tokens for texture and structural tokens for geometry. These tokens jointly condition the diffusion process, enabling the framework to synthesize watermark-free images with fine-grained consistency and structural integrity. Comprehensive experiments show that TokenPure achieves state-of-the-art watermark removal and reconstruction fidelity, substantially outperforming existing baselines in both perceptual quality and consistency.
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                迷失于失真：揭示计算机视觉与脑成像之间的领域鸿沟——一项关于年龄预测预训练的研究
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            Lost in Distortion: Uncovering the Domain Gap Between Computer Vision and Brain Imaging - A Study on Pretraining for Age Prediction
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yanteng Zhang, Songheng Li, Zeyu Shen, Qizhen Lan, Lipei Zhang, Yang Liu, Vince ...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于计算机视觉与脑成像之间的领域差异，并研究年龄预测的预训练方法。这属于医学/生物学领域的特定应用，与推荐系统、搜索或广告的技术核心无关。根据用户指定的不相关主题列表，医学、生物学或其他领域特定应用应被排除。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:11:31
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                <a href="https://arxiv.org/abs/2512.01310v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01310v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Large-scale brain imaging datasets provide unprecedented opportunities for developing domain foundation models through pretraining. However, unlike natural image datasets in computer vision, these neuroimaging data often exhibit high heterogeneity in quality, ranging from well-structured scans to severely distorted or incomplete brain volumes. This raises a fundamental question: can noise or low-quality scans contribute meaningfully to pretraining, or do they instead hinder model learning? In this study, we systematically explore the role of data quality level in pretraining and its impact on downstream tasks. Specifically, we perform pretraining on datasets with different quality levels and perform fine-tuning for brain age prediction on external cohorts. Our results show significant performance differences across quality levels, revealing both opportunities and limitations. We further discuss the gap between computer vision practices and clinical neuroimaging standards, emphasizing the necessity of domain-aware curation to ensure trusted and generalizable domain-specific foundation models.
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    <div class="flex justify-between items-start mb-1">
        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01306v1" target="_blank" rel="noopener noreferrer">
                高斯摇摆：基于表面的三维高斯流体动力学仿真框架
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Gaussian Swaying: Surface-Based Framework for Aerodynamic Simulation with 3D Gaussians
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hongru Yan, Xiang Zhang, Zeyuan Chen, Fangyin Wei, Zhuowen Tu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算流体动力学和3D高斯方法，属于物理模拟领域。虽然提到了3D高斯，但这与推荐系统、搜索或广告中的Transformer架构、LLM应用或异构数据处理无直接关联。没有明显的技术可以应用于推荐系统、搜索或广告领域。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 06:03:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01306v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01306v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.GR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Branches swaying in the breeze, flags rippling in the wind, and boats rocking on the water all show how aerodynamics shape natural motion -- an effect crucial for realism in vision and graphics. In this paper, we present Gaussian Swaying, a surface-based framework for aerodynamic simulation using 3D Gaussians. Unlike mesh-based methods that require costly meshing, or particle-based approaches that rely on discrete positional data, Gaussian Swaying models surfaces continuously with 3D Gaussians, enabling efficient and fine-grained aerodynamic interaction. Our framework unifies simulation and rendering on the same representation: Gaussian patches, which support force computation for dynamics while simultaneously providing normals for lightweight shading. Comprehensive experiments on both synthetic and real-world datasets across multiple metrics demonstrate that Gaussian Swaying achieves state-of-the-art performance and efficiency, offering a scalable approach for realistic aerodynamic scene simulation.
                </div>
            </details>
    </div>
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 * @Author: Doragd doragd@users.noreply.github.com
 * @Date: 2025-10-09 23:23:38
 * @LastEditors: Doragd doragd@users.noreply.github.com
 * @LastEditTime: 2025-10-10 00:41:41
 * @FilePath: /Algorithm-Practice-in-Industry/paperBotV2/frontend/templates/normal_paper_template.html
 * @Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
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            <a href="https://www.alphaxiv.org/abs/2512.01296v1" target="_blank" rel="noopener noreferrer">
                EGG-Fusion：基于几何感知高斯面元的实时高效三维重建
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            EGG-Fusion: Efficient 3D Reconstruction with Geometry-aware Gaussian Surfel on the Fly
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaokun Pan, Zhenzhe Li, Zhichao Ye, Hongjia Zhai, Guofeng Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向计算机视觉领域的三维重建技术，属于纯粹的视觉/3D视觉研究。虽然标题中提到“高效”和“实时”可能暗示性能优化，但核心内容与推荐系统、搜索或广告的排名、检索、用户建模等关键技术无直接关联。根据用户列出的无关主题，这属于“纯粹的视觉、3D视觉论文，与推荐/搜索/广告无明显相关性”的范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 05:32:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01296v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01296v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Real-time 3D reconstruction is a fundamental task in computer graphics. Recently, differentiable-rendering-based SLAM system has demonstrated significant potential, enabling photorealistic scene rendering through learnable scene representations such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). Current differentiable rendering methods face dual challenges in real-time computation and sensor noise sensitivity, leading to degraded geometric fidelity in scene reconstruction and limited practicality. To address these challenges, we propose a novel real-time system EGG-Fusion, featuring robust sparse-to-dense camera tracking and a geometry-aware Gaussian surfel mapping module, introducing an information filter-based fusion method that explicitly accounts for sensor noise to achieve high-precision surface reconstruction. The proposed differentiable Gaussian surfel mapping effectively models multi-view consistent surfaces while enabling efficient parameter optimization. Extensive experimental results demonstrate that the proposed system achieves a surface reconstruction error of 0.6\textit{cm} on standardized benchmark datasets including Replica and ScanNet++, representing over 20\% improvement in accuracy compared to state-of-the-art (SOTA) GS-based methods. Notably, the system maintains real-time processing capabilities at 24 FPS, establishing it as one of the most accurate differentiable-rendering-based real-time reconstruction systems. Project Page: https://zju3dv.github.io/eggfusion/
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 * @Author: Doragd doragd@users.noreply.github.com
 * @Date: 2025-10-09 23:23:38
 * @LastEditors: Doragd doragd@users.noreply.github.com
 * @LastEditTime: 2025-10-10 00:41:41
 * @FilePath: /Algorithm-Practice-in-Industry/paperBotV2/frontend/templates/normal_paper_template.html
 * @Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
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    <div class="flex justify-between items-start mb-1">
        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.01292v1" target="_blank" rel="noopener noreferrer">
                用于医学图像分割任务的潜在空间扩散模型
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Diffusion Model in Latent Space for Medical Image Segmentation Task
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huynh Trinh Ngoc, Toan Nguyen Hai, Ba Luong Son, Long Tran Quoc
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学图像分割这一特定领域应用，属于您列出的不相关主题中的“Medical, Biology, Chemistry, Physics or other domain-specific applications”。虽然扩散模型是生成模型的一种，但该论文的应用场景与推荐系统、搜索或广告的核心技术进展、LLM使能技术、Transformer架构改进或直接LLM应用均无明确关联。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-01 05:26:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.01292v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.01292v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Medical image segmentation is crucial for clinical diagnosis and treatment planning. Traditional methods typically produce a single segmentation mask, failing to capture inherent uncertainty. Recent generative models enable the creation of multiple plausible masks per image, mimicking the collaborative interpretation of several clinicians. However, these approaches remain computationally heavy. We propose MedSegLatDiff, a diffusion based framework that combines a variational autoencoder (VAE) with a latent diffusion model for efficient medical image segmentation. The VAE compresses the input into a low dimensional latent space, reducing noise and accelerating training, while the diffusion process operates directly in this compact representation. We further replace the conventional MSE loss with weighted cross entropy in the VAE mask reconstruction path to better preserve tiny structures such as small nodules. MedSegLatDiff is evaluated on ISIC-2018 (skin lesions), CVC-Clinic (polyps), and LIDC-IDRI (lung nodules). It achieves state of the art or highly competitive Dice and IoU scores while simultaneously generating diverse segmentation hypotheses and confidence maps. This provides enhanced interpretability and reliability compared to deterministic baselines, making the model particularly suitable for clinical deployment.
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            // 计算当前月份的第一天是星期几
            const firstDay = new Date(displayYear, displayMonth, 1);
            const firstDayOfWeek = firstDay.getDay();
            
            // 计算当前月份的天数
            const daysInMonth = new Date(displayYear, displayMonth + 1, 0).getDate();
            
            // 添加上月的占位天数
            for (let i = 0; i < firstDayOfWeek; i++) {
                const emptyDay = document.createElement('div');
                emptyDay.classList.add('py-1', 'text-gray-300');
                calendarGrid.appendChild(emptyDay);
            }
            
            // 获取当前日期（用于高亮显示）
            const today = new Date();
            today.setHours(0, 0, 0, 0);
            
            // 添加当前月份的天数
            for (let day = 1; day <= daysInMonth; day++) {
                const dayElement = document.createElement('div');
                const currentDateObj = new Date(displayYear, displayMonth, day);
                const dateStr = displayYear + String(displayMonth + 1).padStart(2, '0') + String(day).padStart(2, '0');
                const displayDateStr = displayYear + '-' + String(displayMonth + 1).padStart(2, '0') + '-' + String(day).padStart(2, '0');
                
                // 设置日期元素基本样式
                dayElement.textContent = day;
                
                // 检查该日期是否有论文数据
                const hasPapers = availableDates.includes(dateStr);
                
                if (hasPapers) {
                    // 有论文数据的日期样式
                    dayElement.classList.add('py-1', 'cursor-pointer', 'hover:bg-gray-100', 'rounded', 'bg-blue-50', 'font-medium');
                    
                    // 添加点击事件，跳转到对应日期的页面
                    dayElement.addEventListener('click', () => {
                        console.log('Date clicked:', displayDateStr);
                        selectedDateText.textContent = displayDateStr;
                        
                        // 保存选择状态到localStorage
                        localStorage.setItem('selectedDate', displayDateStr);
                        localStorage.setItem('selectedYear', displayYear.toString());
                        localStorage.setItem('selectedMonth', displayMonth.toString());
                        
                        datePicker.classList.add('hidden');
                        
                        // 构造目标URL并跳转
                        const targetUrl = 'arxiv_' + dateStr + '.html';
                        window.location.href = targetUrl;
                    });
                } else {
                    // 没有论文数据的日期样式（置灰不可点击）
                    dayElement.classList.add('py-1', 'text-gray-400', 'cursor-not-allowed');
                }
                
                // 高亮显示当天日期（覆盖之前的样式）
                if (currentDateObj.getTime() === today.getTime()) {
                    dayElement.classList.remove('bg-blue-50');
                    dayElement.classList.add('bg-primary', 'text-white', 'font-bold', 'shadow');
                    if (!hasPapers) {
                        // 当天没有论文时，仍然置灰但保持背景色
                        dayElement.classList.add('opacity-70');
                    }
                }
                
                // 高亮显示当前选中的日期
                if (displayDateStr === selectedDateText.textContent) {
                    dayElement.classList.add('font-bold', 'border-2', 'border-primary', 'rounded-lg', 'shadow-md');
                }
                
                // 增强有论文数据的日期样式，使其更明显
                if (hasPapers && currentDateObj.getTime() !== today.getTime()) {
                    dayElement.classList.add('bg-blue-100', 'hover:bg-blue-200', 'transition-colors', 'duration-200');
                }
                
                calendarGrid.appendChild(dayElement);
            }
        }
    }
    </script>
    </body>

</html>